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Insilico partners with Taisho on end-to-end AI-powered senolytic drug discovery – PR Newswire India

Posted: October 15, 2020 at 11:52 pm

"We're delighted to collaborate with Taisho pharmaceutical, a well-recognized leader in the pharmaceutical industry and healthcare sector. It is believed that aging is a universal phenomenon that we cannot stop. However, emerging scientific evidence has shown that one may be able to reverse some of the age-associated processes. Through this collaboration, we will adopt our AI-powered drug discovery suites together with Taisho's validation platform to explore the new space of anti-aging solutions," said Jimmy Yen-Chu Lin, PhD, CEO of Insilico Medicine Taiwan, a fully-owned subsidiary of Insilico Medicine

Under the terms of the agreement, Insilico Medicine will receive an upfront payment and milestone payments upon achievement of specified goals. Insilico Medicine will be responsible for early research phase target identification and molecular generation and Taisho will work collaboratively with Insilico in validating the results in various in vitro and in vivo assays. Taisho has the exclusive option to acquire Insilico's co-ownership of the successfully developed programs under agreed payment.

"It is our great honor to be collaborating with the scientists of Taisho Pharmaceutical, one of the top 100 pharmaceutical companies in the world operating since 1912. The high level of the scientists we are interfacing, and our previous successes in the application of the Pharma.AI platform for discovery of novel targets and molecules in fibrosis, and previous experience in senolytic drug discovery give us confidence that this collaboration will be successful," said Alex Zhavoronkov, PhD, founder and CEO of Insilico Medicine.

About Taisho Pharmaceutical Co., Ltd.

https://www.taisho.co.jp/global/

Media Contact

For further information, images or interviews, please contact:

[emailprotected]

About Insilico Medicine

Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Recently, Insilico Medicine secured $37 million in series B funding. Since its inception, Insilico Medicine raised over $52 million, published over 100 peer-reviewed papers, applied for over 25 patents, and received multiple industry awards. Website http://insilico.com/

Photo - https://mma.prnewswire.com/media/1312850/Insilico_Taisho.jpg

http://insilico.com

SOURCE Insilico Medicine Hong Kong Limited

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Insilico partners with Taisho on end-to-end AI-powered senolytic drug discovery - PR Newswire India

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OpGen Provides Business and Pipeline Update and Announces Preliminary Unaudited Revenue and Cash Position for the Third Quarter 2020 – BioSpace

Posted: October 15, 2020 at 11:52 pm

GAITHERSBURG, Md., Oct. 15, 2020 (GLOBE NEWSWIRE) -- OpGen, Inc.. (Nasdaq: OPGN, OpGen), a precision medicine company harnessing the power of molecular diagnostics and bioinformatics to help combat infectious disease, announced today that total preliminary unaudited revenue for the third quarter of 2020 was approximately $1.0 million, up from $648 thousand in the third quarter of 2019. The preliminary financial results for the three months ended September 30, 2020 reflect the consummation of our business combination with Curetis GmbH on April 1, 2020. The results for the nine months ended September 30, 2020 will be included in the Companys Quarterly Report on Form 10-Q and earnings release for the third quarter of 2020. OpGens cash as of September 30, 2020 was approximately $10.4 million. The company also expanded its capacity under its ATM program by an additional $6.4 million, and continues to have access to an additional EUR5.0 million tranche of non-dilutive debt financing for COVID-19 related R&D programs from the European Investment Bank.

In addition, the company announced details regarding a strategic reprioritization of its product portfolio, platform pipeline and priorities going forward. This reprioritization was based on feedback from extensive market research, a customer survey of 150 stakeholders in the decision making on new diagnostic platforms, and key opinion leader interviews conducted by an independent market research firm over the past two quarters. Following a review of this research, OpGen and its Board decided to consolidate the companys product portfolio on its proprietary Unyvero platform and unique bioinformatics capabilities. As a result of this change in priority, the company anticipates the following key impacts:

The company also announced accomplishment of the following key milestones in the third quarter of 2020 and year to date:

Oliver Schacht, President & CEO of OpGen commented, OpGen reported a solid third quarter given the persistent challenging environment caused by the COVID-19 pandemic. In addition to announcing the CE mark certification for our SARS-CoV-2 Kit, we also highlighted the publication of several peer-reviewed studies. We believe that following the portfolio consolidation and strategic product pipeline decisions taken by the board, OpGen along with its subsidiary companies Curetis GmbH and Ares Genetics GmbH has a focused molecular diagnostics platform strategy and growing emphasis on bioinformatics offerings that will further generate shareholder value. I am truly excited about the future prospect of this company and I am convinced that our strategic initiatives will provide strong growth opportunities and secure our future as a global leader in infectious diseases and AMR diagnostics.

The preliminary financial results are estimates prior to the completion of OpGensfinancial closing procedures and review procedures by its external auditors and therefore may be subject to adjustment when the actual results are available.

About OpGen, Inc.

OpGen, Inc. (Gaithersburg, MD, USA) is a precision medicine company harnessing the power of molecular diagnostics and bioinformatics to help combat infectious disease. Along with subsidiaries, Curetis GmbH and Ares Genetics GmbH, we are developing and commercializing molecular microbiology solutions helping to guide clinicians with more rapid and actionable information about life threatening infections to improve patient outcomes, and decrease the spread of infections caused by multidrug-resistant microorganisms, or MDROs. OpGens product portfolio includes Unyvero, Acuitas AMR Gene Panel and Acuitas Lighthouse, and the ARES Technology Platform including ARESdb, using NGS technology and AI-powered bioinformatics solutions for antibiotic response prediction.

For more information, please visit http://www.opgen.com.

Forward-Looking Statements

This press release includes statements regarding OpGens third quarter 2020 results, the companys strategic portfolio and product pipeline priorities, the ongoing integration of OpGen with its acquired subsidiaries, Curetis GmbH and Ares Genetics GmbH, and the impact of COVID-19 on the company and general market conditions. These statements and other statements regarding OpGens future plans and goals constitute "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934 and are intended to qualify for the safe harbor from liability established by the Private Securities Litigation Reform Act of 1995. Such statements are subject to risks and uncertainties that are often difficult to predict, are beyond our control, and which may cause results to differ materially from expectations. Factors that could cause our results to differ materially from those described include, but are not limited to, our ability to successfully, timely and cost-effectively develop, seek and obtain regulatory clearance for and commercialize our product and services offerings, the rate of adoption of our products and services by hospitals and other healthcare providers, the realization of expected benefits of our business combination transaction with Curetis GmbH, the success of our commercialization efforts, the impact of COVID-19 on the Companys operations, financial results, and commercialization efforts as well as on capital markets and general economic conditions, the effect on our business of existing and new regulatory requirements, and other economic and competitive factors. For a discussion of the most significant risks and uncertainties associated with OpGen's business, please review our filings with the Securities and Exchange Commission. You are cautioned not to place undue reliance on these forward-looking statements, which are based on our expectations as of the date of this press release and speak only as of the date of this press release. We undertake no obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future events or otherwise.

OpGen:Oliver SchachtPresident and CEOInvestorRelations@opgen.com

OpGen Press Contact:Matthew BretziusFischTank Marketing and PRmatt@fischtankpr.com

OpGen Investor Contact:Megan PaulEdison Groupmpaul@edisongroup.com

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Study reveals the role of our second brain in diabetes – Medical News Today

Posted: October 13, 2020 at 8:00 pm

Researchers have uncovered new clues to the mystery of how the guts nervous system affects glucose metabolism in the rest of the body. Their findings could lead to new treatments for type 2 diabetes.

Type 2 diabetes causes the bodys cells to become less sensitive to signals from insulin, the hormone responsible for regulating levels of glucose in the blood.

This low sensitivity is called insulin resistance, and it keeps the cells from absorbing the extra glucose that enters the bloodstream after a meal.

Over time, high concentrations of glucose in the blood damage tissues all over the body, causing complications such as heart disease, vision loss, and kidney disease.

The Centers for Disease Control and Prevention (CDC) estimate that more than 30 million people in the United States have type 2 diabetes.

Changes to the diet, exercise, and other aspects of life can improve symptoms and even reverse the condition in some people. Drugs are also available to treat type 2 diabetes, but they can cause side effects such as nausea and diarrhea.

Another drawback to some antidiabetic drugs is that they have to be injected.

Discovering oral treatments that are not only effective but also free of side effects is therefore a priority for diabetes researchers.

Now, a group of scientists, many affiliated with the French National Institute of Health and Medical Research, or INSERM, in Toulouse, believe that they are a step closer to developing such a treatment. They have published their findings in the journal Gut.

This latest research builds on previous work suggesting that fat, or lipid, molecules produced by friendly gut bacteria can improve blood glucose metabolism.

These lipids are thought to influence the gut-brain axis the vital two-way communication between the brain and the guts highly developed nervous system, also known as the enteric nervous system or second brain.

In type 2 diabetes, communication between the gut and brain appears to break down. As a result, after a meal, the brain fails to send signals to the liver, muscles, and fat tissue telling them to absorb more glucose from the bloodstream. This, in turn, leads to insulin resistance.

Normally the duodenum, the first part of the small intestine, signals to the brain, which involves a relaxation of the smooth muscles in its lining. In individuals with type 2 diabetes, however, these muscles are permanently contracted, or hypercontractile, so the signal is never sent.

The researchers believe that friendly gut bacteria are the key to reversing hypercontractility and restoring healthy glucose metabolism.

Nutrients that feed friendly bacteria are called prebiotics. In particular, carbohydrates called fructooligosaccharides (FOS) are known to promote the growth of bacteria that improve glucose metabolism through the production of various lipids.

However, the identity of these lipids has remained unknown until now.

To find out more, the researchers fed mice a special diet supplemented with FOS. Then, they compared the contents of their colons with those of mice that did not receive supplementary FOS.

The team discovered that the only lipid with significantly increased levels in the colons of the FOS mice was a lipid called 12-HETE.

When they fed 12-HETE to diabetic mice, the lipid not only reduced duodenal hypercontraction but also improved the mices blood glucose levels.

To explore whether these results applied to humans, the scientists analyzed biopsies from the duodenums of people with type 2 diabetes who had received antidiabetic treatments and those of healthy volunteers who had not.

They found that there was 38% less 12-HETE in the duodenums of the people with diabetes, compared with the healthy volunteers. The researchers acknowledge that this finding was not statistically significant, but also point to the small numbers of volunteers in their study.

Finally, they showed that 12-HETE reduces muscle contraction in the duodenum by boosting the signal from a nerve receptor called the mu-opioid receptor. This restored communication between the gut and the brain.

This study is one of the latest to reveal intimate relationships between the bacteria in the human gut, known collectively as the microbiota, and our health.

The scientists are optimistic that their work will inspire new treatments, which could either boost production of 12-HETE in the gut or involve taking the lipid orally, as a supplement.

In their paper, the researchers conclude:

Using a combination of nutritional and pharmacological approaches, we have identified a new mode of communication between gut microbes and the host. In addition, we have identified novel targets and their mechanisms of action in rodents, and possibly in humans. The identification of specific targets [] to treat [type 2 diabetes] and its comorbidities represent a groundbreaking solution to develop medications without side effects.

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Here Are The Early Signs of Diabetes – Yahoo Lifestyle

Posted: October 13, 2020 at 8:00 pm

So many people have diabetesabout 1.5 million are diagnosed in the United States each year, and nearly 1 in 10 Americans have ityou'd think it'd be easy to spot. But although the condition is relatively common, many people go undiagnosed because the early symptoms can be vague, easily overlooked at first, or confused with other conditions.

Here from Eat This, Not That! Health are the first signals your body might send when you develop diabetes. Read on, and to ensure your health and the health of others, don't miss these Sure Signs You've Already Had Coronavirus.

A very common early sign of diabetes, increased thirst happens because diabetes causes sugar (glucose) to build up in the bloodstream. Normally, the kidneys process glucose, but when they become overwhelmed, the excess glucose is flushed out with your urine. Water from other body tissues is pulled along with it, leaving you dehydrated and craving fluids to replace what you've lost.

The Rx: Experts such as Harvard Medical School advise drinking four to six cups of water per day. If you're hydrating adequately but you've noticed an uptick in thirst, talk with your doctor.

In early diabetes, the body will increase urine production, attempting to flush out that excess blood sugar, and you might find yourself having to go more often. "It's important to know what is normal for your body," says Leigh Tracy, RD, LDN, CDE, a registered dietitian and diabetes program coordinator at Mercy Medical Center in Baltimore. "The average individual urinates between seven and eight times per day, but for some, up to 10 times per day is normal."

The Rx: "If you are urinating more than your norm, and especially if you are waking up multiple times in the middle of the night to urinate, speak with your primary care physician right away," says Tracy.

Diabetes causes blood glucose to rise uncontrollably. At the same time, it prevents cells from using glucose for energy. That lack of energy can make you hungry.

Story continues

The Rx: "If you notice you're constantly hungry even though you have just eaten regular meals and snacks during the day, you should speak with your doctor," says Tracy.

RELATED: 11 Symptoms of COVID You Never Want to Get

Because diabetes elevates blood sugar at the same time it prevents the body from using it for energy, that can make you fatigued. Frequent urination can also disrupt your sleep.

The Rx: There's a difference between tiredness and fatigue. Normal tiredness gets better after rest. But if you still feel worn out despite getting an adequate amount of sleep, it's worth discussing with your doctor.

According to the Mayo Clinic, high levels of blood glucose pull fluid from your tissues, including the lenses of your eyes. This can affect your ability to focus and cause blurry vision. Diabetes can also cause new blood vessels to form in the retinas, damaging established vessels. If those changes progress untreated, they can lead to vision loss.

The Rx: If you're experiencing any signs of diabetes such as blurred vision, it's important to see your doctor ASAP, and regularly if you're diagnosed. "Diabetes is a progressive disease, even in patients with excellent lifestyles," says Sarah Rettinger, MD, an endocrinologist at Providence Saint John's Health Center in Santa Monica, California.

Diabetes can make skin injuries, such as cuts and bruises, slower to heal. High blood sugar can stiffen blood vessels, slowing blood flow and preventing oxygen and nutrients from getting to cuts and bruises to heal them. Diabetes can also impair the immune system, slowing the body's natural repair processes.

The Rx: If you notice that cuts or bruises aren't healing as quickly as they have in the past, see your healthcare provider.

RELATED: Worst Things For Your HealthAccording to Doctors

Losing weight without any changes in diet or exercise may sound great, but it's the definition of too good to be true: It can signify a serious health condition such as hyperthyroidism, cancer or diabetes. When diabetics lose glucose through frequent urination, they also lose calories. Diabetes may also keep cells from absorbing glucose from food for energy, and the body may begin to burn its fat stores as fuel instead. Both can result in weight loss.

The Rx: If you're shedding pounds without trying, see your doctor and ask if you should be tested for diabetes.

Diabetes can lead to a kind of nerve damage called neuropathy, which can cause tingling or numbness in your extremities like hands or feet. This is dangerous because numbness can make cuts or injuries easier to overlook, and because diabetes can cause wounds to heal more slowly, complications can result.

The Rx: Be aware of what's going on with your body, and if you're experiencing any unusual pain, numbness or tingling in your hands or feet, see a healthcare provider without delay.

RELATED: 11 Signs You Need to Go the ERBy an ER Doctor

"People often have no symptoms of diabetes," says Kristine Arthur, MD, an internist at MemorialCare Medical Group in Irvine, California. "Sometimes they may notice weight gain, persistent hunger and increased fatigue associated with high insulin levels, but these symptoms can be present in other conditions, so it is important to have blood tests done to find out what is the cause."

The Rx: Have your HgbA1c (sometimes called "A1c") levels checked with a blood test every year during your routine checkup.

And to get through this pandemic at your healthiest, don't miss these 35 Places You're Most Likely to Catch COVID.

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Easier-to-use technology helps young people with type 1 diabetes – Scope

Posted: October 13, 2020 at 8:00 pm

Technology for treating type 1 diabetes is becoming easier to use, an especially beneficial change for teens and young adults, according to a recent study published in JAMA.

The clinical trial, conducted at Stanford Medicine and 13 other U.S. institutions, analyzed whether the newest continuous blood glucose monitors are helpful for patients aged 14 to 24. The monitors rely on a wearable sensor that measures blood-sugar levels once every five minutes and sends the readings to a receiver or smartphone.

The study found that teens and young adults using these monitors had better long-term blood sugar control than those who used manual blood-sugar monitoring. That's big news because teens and young adults tend to have the most difficulty with blood-sugar control, said study co-author Priya Prahalad, MD, a pediatric endocrinologist at Stanford Children's Health.

"A big struggle exists for this age group: They want to be just like their friends, to live a normal life and snack and eat like their friends do, and they're self-conscious about being different from their peers," Prahalad said.

With the new technology, instead of pulling out a glucometer, teens can check their blood sugars discreetly with a quick peek at their phones.

Type 1 diabetes interferes with the body's ability to make insulin, a hormone that helps glucose -- the main sugar in blood -- move into muscle and other tissues for use as an energy source.

People with type 1 diabetes must check their blood sugar levels several times per day, including before eating, then calculate how much insulin to inject. Handheld glucometers require frequent, painful finger pricks to get a drop of blood for testing. Also, because middle-of-the-night checks are needed to catch dangerously low sugar levels, it's difficult for patients and their parents to get a good night's sleep.

The newest continuous glucose monitoring systems rely on a wearable sensor with a thin platinum wire inserted just under the patient's skin, so patients don't have to prick their fingers.

The wire stays in place for 10 days, including while patients are bathing or swimming; changing it out is about as painful as an insulin injection, Prahalad said. The system records blood glucose once every five minutes, or 288 times per day. Patients can easily share the data by having it sent to a parent's phone as well as their own.

In the new study, 153 participants were randomly assigned to use either continuous monitoring systems or a handheld glucometer for six months. Most patients were consistent about using the automated technology until the end of the trial. For those who were, glycated hemoglobin levels, an indicator of long-term blood glucose control, were lower.

There are even bigger improvements ahead, Prahalad said.

"We're in a phase of diabetes research where we are developing more automated insulin-delivery systems, with insulin pumps that use continuous glucose monitoring as a key component," she said.

Using continuous monitoring sets teens and young adults up to take advantage of the next wave of "smart" diabetes devices, she said, adding "This is the future of diabetes care."

Photo by Yura Fresh

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Easier-to-use technology helps young people with type 1 diabetes - Scope

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Diabetes? This Rajma And Chana Chaat Could Be The Yummiest Addition To Your Diet – NDTV Food

Posted: October 13, 2020 at 8:00 pm

Highlights

It would not be an exaggeration to say that diabetes has been one of the most talked about health conditions, as far as this decade is concerned. Of course, the condition is not new, but the rise in number of people, especially young people, being diagnosed with the metabolic condition has been a worrisome affair. A study, published in the Lancet Journal in the year 2018, predicted that about 98 million Indians stand at a risk of developing diabetes by the year 2030.

(Also Read:Diabetes Diet: Neem Tea May Help Manage Blood Sugar Levels - Recipe Inside)

Diabetes is a condition where your body is either not producing enough insulin, or not responding to the insulin produced. Unfortunately, there are not many known treatments to reverse the condition, so all you can do is manage the symptoms by eating right and maintaining a healthy lifestyle. Diabetics should include more and more fibre-rich foods in their diet. This is because fibre-rich foods digest slowly and thus enable slow release of sugar in the bloodstream, and keep blood sugar spikes in control.

(Also Read:Diabetes Diet: 17 Easy Diabetes-Friendly Snack Ideas To Manage Blood Sugar Levels)

Take a note of the time when you are checking your blood sugarsPhoto Credit: iStock

Diabetes Diet:

If you look around, you would find plenty of natural food sources for good quality fibre. Most fruits and green vegetables are enriched with fibre, even the legumes like chana and rajma are not far behind. Not just fibre chana (chickpea) and rajma (kidney beans) are also good sources of protein and inflammation-fighting antioxidants. While you know very well how to use them to make delightful curries, have you ever tried whipping up a quick chaat using the two? Here's a toothsome recipe you would thank us for.

Diabetes Diet: How To Make Chana-Rajma Chaat

Ingredients:

Promoted

Method:1. In a large mixing bowl, add the chana and rajma together. Then add the tomatoes, onions and sweet potatoes.2. Now, throw in the coriander leaves, chutney, chaat masala and mix everything well.3. Top it up with a pinch of lemon juice and chomp away.

Try making this chaat at home, and do not refrain from experimenting. You can add and eliminate ingredients of your choice. It is your chaat after all! Do tell us how you liked it in the comments below.

(This content including advice provides generic information only. It is in no way a substitute for qualified medical opinion. Always consult a specialist or your own doctor for more information. NDTV does not claim responsibility for this information.)

About Sushmita SenguptaSharing a strong penchant for food, Sushmita loves all things good, cheesy and greasy. Her other favourite pastime activities other than discussing food includes, reading, watching movies and binge-watching TV shows.

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Perspectives on the psychological and emotional burden of having gestational diabetes amongst low-income women in Cape Town, South Africa – BMC Blogs…

Posted: October 13, 2020 at 8:00 pm

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The role of mHealth and digitisation in diabetes care – Med-Tech Innovation

Posted: October 13, 2020 at 8:00 pm

Martin Gerber, global head of innovation at Ascensia Diabetes Care, writes about the use of digital health technology in helping treating diabetes patients.

The number of mobile health (mHealth) apps continues to soar year on year, with more than 300,000 currently available on iOS and Android compared with the approximately 165,000 available in 2015.

Digital tools such as apps are becoming increasingly important for the 463 million people across the globe with diabetes, helping many to manage their condition. But as mHealth becomes more established in healthcare and as adoption is accelerated by the ongoing COVID-19 pandemic, we need to consider how to optimise its impact for all.

mHealth and diabetes care

Most people with diabetes (PWDs) only see their healthcare professional (HCP) for a few hours each year, making the majority of care self-management. Its therefore no surprise that there are approximately 2,000 apps in the US alone related to supporting people with diabetes. Crucially, in the absence of regular facetime with HCPs, some of these apps enable PWDs to monitor their diabetes management, identify patterns and trends, and share data with their HCPs when they need more input.

These mHealth apps play a key role in self-management and there is evidence to show they really do work. For example, we found in a study from 2018 that using Ascensias Contour Diabetesapp for over 180 days was associated with a reduced frequency of both hypoglycaemic and hyperglycaemic events.

Furthermore, apps can provide vital support in driving long-term behavior change in areas such as nutrition, activity and sleep that not only improve diabetes but also its related comorbidities. This is where digital health solutions have great potential to offer holistic approaches that deliver sustainable, optimal health outcomes.

Creating a holistic ecosystem

It is key to keep in mind that mHealth apps cannot be used in isolation they must be employed as part of a holistic healthcare ecosystem.

Digital health is about connecting the dots between different devices, conditions and treatments as well as between patients and HCPs. As a result, partnerships and collaborations play a crucial role. From digital health start-ups to healthcare providers, organisations need to ensure that their devices and systems integrate together if they are to improve the quality of life for those that depend on them.

As an example, for PWDs this could include enabling data integration from multiple sources such as CGMs, ECGs, and other wearables into the same app or digital solution. From this, treatments and management advice can be tailored to the bespoke needs of the patient.

A data driven society

In todays society, data drives many of the biggest discoveries and healthcare is no exception. Huge advances in technology are now arming HCPs and patients with vast amounts of data and its becoming apparent that this is leading to better health insights.

The future of diabetes management, for example, is more than just devices and medications. Collecting and analysing data to provide actionable, personalised management recommendations is critical to improving the health and lives of PWDs. Yet less than 1% of mHealth apps related to diabetes in the US have more than 50,000 monthly active users (MAUs), and less that 10% of mHealth apps across the board have exceeded this level either.

The challenges for developers offering mHealth apps remain unchanged: how to engage, re-engage, and retain regular users.

From app recruitment to retention

Some fundamental barriers to app usage are harder to address than others. Around the world many people dont have access to the necessary technology, such as smartphones and laptops. This is further compounded by digital and health literacy issues that create a serious barrier to virtual diabetes care.

It is imperative for apps to retain those who do have access as they can improve a persons engagement in their care. In a study we found that engagement with diabetes self-management increased over time, as demonstrated by frequency of blood glucose testing after 180 days of app use versus 30 days. In other words, long-term use of a mHealth app seems to facilitate higher engagement by people with chronic conditions and, in turn, this can lead to better self-management.

The user journey is generally the most influential factor when considering how best to retain and engage users. Apps must fit into the lifestyles of those managing chronic conditions and listening to users is paramount in establishing what features matter. This is where user centred design and frequent usability studies become critical to the development process.

In healthcare, needs vary from person to person, through gender, age and socioeconomic situation. A truly personalised user experience goes beyond medicine in order to drive engagement, and ultimately improve quality of life.

For the health ecosystem to really thrive, we need to increase access to mHealth and enable better collaboration that can optimise digitisation. We are starting to see this in abundance in diabetes care and we are very excited to see this transformation stretch above and beyond diabetes management.

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Emerging Data on Type 1 Diabetes and COVID-19 Reassuring – Medscape

Posted: October 13, 2020 at 8:00 pm

Editor's note: Find the latest COVID-19 news and guidance in Medscape's Coronavirus Resource Center.

Most people with type 1 diabetes do not appear to be at increased risk for hospitalization or death from COVID-19 compared to the general population, new research suggests.

Two retrospective studies of type 1 diabetes and COVID-19 were published in the October issue of Diabetes Care.

One, by Roman Vangoitsenhoven, MD, PhD, of University Hospitals Leuven, Belgium, and colleagues, found no evidence of increased hospitalization for COVID-19 among people with type 1 diabetes during the first 3 months of the pandemic in Belgium.

The other, from Maria Vamvini, MD, of the Joslin Diabetes Center, Boston, Massachusetts, and colleagues, showed that age and glycemic control didn't differ significantly between adults with type 1 diabetes hospitalized for COVID-19 and those hospitalized for other reasons.

Previous data from the UK Biobank and the Type 1 Diabetes (T1D) Exchange support these findings.

Altogether, these results suggest that although the risk for death from COVID-19 is higher overall among people with type 1 diabetes, that increased risk is mostly limited to a subset of particularly vulnerable patients, said Catarina Limbert, MD, PhD, during a press briefing at the virtual annual meeting of the European Association for the Study of Diabetes (EASD).

"Those with type 1 diabetes dying from COVID-19 were a specific population," stressed Limbert, of the University Center of Central Lisbon and Hospital Dona Estefania, Lisbon, Portugal.

"They had hemoglobin A1c levels above 10% and were over age 50 with a long diabetes duration. They were the more fragile, who couldn't survive the severity and aggressiveness of the virus. Good glucose control is a good sign and protective," she added.

Daniel Drucker, MD, of Mount Sinai Hospital, Toronto, Canada who spoke at the EASD press briefing regarding potential mechanisms involved in COVID-19 morbidity in diabetes reiterated the importance of glycemic control.

He showed a slide with the following advice for patients with both types of diabetes during the pandemic in addition to the general and now-familiar physical distancing, personal hygiene, hand washing, and wearing of masks:

Prepare a list of all medications, written and on the phone.

Consider supplies of medications, test strips, and continuous glucose monitoring equipment.

Don't neglect exercise, diet, and blood glucoseand blood pressure control.

Use telemedicine and devices to communicate with healthcare professionals.

Maintain appropriate levels of hydration, exercise, and glucose and ketone monitoring.

Optimize glycemic control whenever possible.

In hospitalized patients with type 2 diabetes, medications may need adjustment. Insulin is often the preferred glucose-lowering prescription.

In the Belgian study, medical records were analyzed for a total of 2336 patients with type 1 diabetes who received care at two specialist diabetes centers. The hospital admission rate was compared with national population data.

Overall, 0.21% (n = 5) of the patients with type 1 diabetes were admitted to the hospital with COVID-19, similar to the 0.17% (n = 15,239) of the general population, as of April 30, 2020 (P = .76).

During the same period, 127 individuals with type 1 diabetes were hospitalized for reasons other than COVID-19, including poor glycemic control (22%), diabetic ketoacidosis (8%), planned surgery (21%), diabetic foot problems (5%), and delivery (5%).

"It is noteworthy that the number of hospitalizations for reasons other than COVID-19 exceeded by far the number of COVID-19related hospitalizations," Vangoitsenhoven and colleagues write.

"Interpretation of adverse outcomes of people with type 1 diabetes during the COVID-19 epidemic should therefore be performed cautiously, as overinterpretation of the impact of COVID-19 itself on adverse outcomes in people with type 1 diabetes is likely," they conclude.

The Boston study, which was smaller, involved retrospective chart reviews of seven patients with type 1 diabetes hospitalized with COVID-19 and another 28 patients hospitalized for other reasons, all during the period from March to May 2020. The groups didn't differ in outpatient insulin doses corrected for weight or in glycemic control in the months preceding admission.

Diabetic ketoacidosis (DKA) occurred in one patient with COVID-19 and in two of the non-COVID patients. Both groups had significant preexisting diabetes-related complications, including nephropathy in more than half of each group and receipt of an organ transplant with immunosuppression in 14% of each group.

The composite outcome intensive care unit (ICU) admission, intubation, or death occurred in two COVID-19 patients (both cases involved ICU admission without intubation, and both patients recovered) and in four non-COVID patients, of whom two died.

The two groups showed "remarkable" similarity in age and glycemic control, although the COVID-19 patients were more likely to be Black (four vs two), consistent with other retrospective studies.

None of the patients had new-onset type 1 diabetes, which contrasts with the 15% seen in the T1D Exchange study.

Just 1 of the 7 patients with COVID-19 (14%) had DKA, compared with 30% of the confirmed and probable COVID-19 patients in the T1D Exchange study.

The significant difference in age about 52 years in the current study vs 21 years in the T1D Exchange study might explain those differences, Vamvini and colleagues say.

Limbert has received grants and personal fees from Abbott, Ipsen, and Sanofi. Vangoitsenhoven has disclosed no relevant financial relaitonships. Vamvini was supported by the National Institute of Diabetes and Digestive and Kidney Diseases. Drucker receives research support, consulting fees, and/or lecture fees from Novo Nordisk, Merck, Pfizer, and Intarcia.

Diabetes Care. 2020 Oct;43:e118-e119. Vangoitsenhoven et al, Full text; 2020 Oct;43:e120-e122. Vamvini et al, Full text

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A 3D atlas of the dynamic and regional variation of pancreatic innervation in diabetes – Science Advances

Posted: October 13, 2020 at 8:00 pm

INTRODUCTION

Insulin-producing cells do not exist in isolation, and their environment has substantial effects on their architecture and function. In addition to the adjacent , delta, ghrelin, pancreatic polypeptide, and other endocrine cells, the exocrine pancreas, vasculature, and innervation all modify cell organization and insulin release (1). Islets are innervated by autonomic parasympathetic and sympathetic fibers, as well as by sensory fibers (2, 3). Evidence from many studies over the past century has identified a critical role for neural signals in modulating insulin and glucagon release to regulate blood glucose (4). For example, anticipatory signals increase insulin release upon food consumption but before any changes in blood glucose, and neural signals suppress insulin and stimulate glucagon release to counteract hypoglycemia (4). Since central nervous system (CNS) and nerve stimulation studies demonstrate that neural signals can override the effects of circulating glucose (5, 6), neural modulation is an attractive target for therapies to improve metabolic control.

Our current understanding of islets and their innervation largely relies on traditional histological techniques using immunolabeled structures in thin sections. These studies have provided a wealth of knowledge about islet structure at high resolution. However, pancreata are highly heterogeneous (7), with distinct regional embryological origins. Sections also lack landmarks to precisely and consistently identify the location of internal structures (8). Until now, laborious serial sectioning and reconstruction have been needed to deliver information about islet anatomy throughout the pancreas. In addition, thin filamentous structures, such as nerves, are difficult to quantify and trace over large volumes using this approach. Recent studies have applied confocal imaging of small pieces and thick sections of cleared pancreatic tissue to examine endocrine innervation (914). These have revealed dense nerve processes within both mouse and human islets. However, given the heterogeneity in the pancreas, there is a clear need for high-resolution, organ-wide imaging to accurately quantify and map regional variation and to assess the three-dimensional (3D) relationship between islets and their environment in health and disease.

Here, we used a tissue-clearing technique, iDISCO+ (15), to determine the 3D distribution of insulin-producing cells, glucagon-producing cells, and neurofilament 200 kDa (NF200)positive innervation across the whole pancreas in healthy animals and in mouse models of diabetes. NF200 is a pan-neuronal marker expressed in sympathetic, sensory, and vagal neurons but, unlike other neural markers, is not expressed in pancreatic endocrine cells (1618). NF200 is expressed in small and large myelinated and small unmyelinated fibers (19), so examining NF200+ fibers provides a comprehensive overview of pancreatic innervation. In addition, NF200 protein levels are altered by nerve damage and repair (2022), so NF200 intensity may reflect remodeling of pancreatic nerves. Using whole-organ 3D imaging and analysis, we readily quantified cell volume and provide detailed information about islet distribution and heterogeneity in mouse and human pancreatic tissue from healthy and diabetic donors. We quantified the dense endocrine innervation and its regional variation and demonstrated significant differences between innervated and noninnervated islets. Islet nerve density is significantly increased in diabetic nonobese diabetic (NOD) mice, with streptozotocin (STZ) treatment, and greater in pancreatic tissue from diabetic human donors. We systematically quantified intrapancreatic ganglia and nerve contacts with and cells to demonstrate that these are largely preserved in diabetes. These findings constitute a 3D atlas of pancreatic innervation for pancreas and diabetes investigators examining pancreatic innervation, the regional heterogeneity in the healthy pancreas, and responses to metabolic disease. Our studies suggest that diabetes is associated with significant remodeling of neural inputs into islets and that neural contacts with endocrine cells are preserved in diabetes.

We applied tissue clearing and whole-organ 3D imaging to examine cell mass, expressed as cell volume, and islet number, as well as spatial distribution in whole pancreata from C57BL/6 mice (Fig. 1, A to C, and movies S1 and S2).

(A) Pancreatic dissection. Photo credit: A.A., Icahn School of Medicine at Mount Sinai. (B) Duodenal (left) and splenic (right) pancreas, maximum projection (1.3). Scale bars, 500 m. (C) Pancreata, maximum projection at 4 (left) and 12 (right). Scale bars, 500 and 200 m. (D) cell volume. (E) Insulin+ islets per cubic millimeter. (F) Insulin intensity (normalized to whole pancreas). (G) Insulin+ islet volume distribution (left axis) and median volume (right axis). Islets per group: 27,092/12,260/14,832. (H) 3D projection of insulin, NF200+ exocrine innervation, and NF200+ surfaces within insulin+ islets (yellow). (I) Exocrine nerve volume. (J) Endocrine nerve volume per insulin+ islet. (K) Endocrine nerve volume/islet volume. (L) Left: 3D model of pancreatic innervation (NF200, white) and insulin (green). Right: Distance transformation analysis with islet surfaces pseudocolored based on distance from the nearest nerve surface. Scale bar, 500 m. Boxed area magnified in the right panel. Scale bar, 200 m. Data are shown as means SEM or median 95% confidence interval as indicated. Analyses by unpaired t test, *P < 0.05 and **P < 0.01. T, total; D, duodenal; S, splenic. N = 7 (D to G) and N = 5 (I to K).

The total cell volume made up 1.31 0.17% of the total pancreatic volume (Fig. 1D), with a greater cell volume in the splenic region. In line with previous reports (23, 24), there were 3874 264.2 islets per pancreas, with 1822 230.4 in the duodenal and 2052 129 in the splenic regions. Islet density (islet number per cubic millimeter) did not differ significantly across the pancreas (Fig. 1E). Insulin intensity showed significant regional variation with intensity in the duodenal pancreas being 25% greater than that in the splenic region (Fig. 1F).

We next examined islet distribution throughout the pancreas to determine whether there were regional differences in cell volume per islet (Fig. 1G). Islets with cell volumes between 1000 and 50,000 m3 were the most abundant (39.29%), followed by islets in the 50,000 to 499,999 m3 range (36.58%). Very large islets (>500,000 m3) comprised 20% of the islet population, and insulin+ structures with volumes below 1000 m3, consisting of five or fewer cells, were the least abundant (3.13%).

There are reported differences in the origins of nerves innervating the duodenal and splenic pancreas (25). Therefore, we hypothesized that there may be regional variations in pancreatic innervation. Thus, we next analyzed the 3D distribution of the pan-neuronal marker NF200 in the healthy mouse pancreas to determine regional variations and relationship to islets (Fig. 1H and movies S3 to S5).

The exocrine nerve volume was 40% greater in the duodenal pancreas compared with the splenic region (Fig. 1I). Pancreatic islets were highly innervated compared to exocrine tissue, with an endocrine nerve density over sixfold greater than the exocrine nerve density. In addition, there was significant regional variation in islet innervation. Nerve volume per islet in the duodenal region was almost double that in the splenic region (Fig. 1J). This difference was more pronounced when the endocrine nerve volume was corrected for cell volume (Fig. 1K). These findings are consistent with marked regional variation in the density of islet innervation.

The proximity of nerves and endocrine cells may have important biological consequences. Autonomic neurotransmission occurs over 1 to 2 m (26), but endocrine and immune cells may influence nerve growth, repair, and function over longer ranges (27, 28). As a result, we examined the proportion of islets in contact with NF200+ fibers and the distance of each islet from the closest NF200+ fiber (Fig. 1L and movie S6). Only 6.1% of islets contained or were in contact with NF200+ fibers, with no significant difference between duodenal and splenic regions (Fig. 2A). The proportion of innervated (NF200+) islets increased with islet volume (fig. S1C). Most islets were within 250 m of an NF200+ fiber, and islets in the duodenal pancreas were significantly closer to nerves than those in the splenic pancreas (fig. S1A).

(A) Distribution of insulin+ islets (<1.6 and >1.6 m from the nearest nerve). Islets per group: 25,310/10,030/15,280. (B) Mean insulin+ islet volume NF200+ innervation; islets per group: 11,869/929/4690/325/7179/604. (C) Total insulin+ islet volume NF200+ innervation. (D) NF200 intensity sum normalized for insulin+ islet volume; islets per group: 5174/4530/2264/687/2196/1788/701/330/2978/2742/1563/357. (E) Intrapancreatic ganglia (NF200, magenta) and cells (insulin, green). Arrows mark ganglia. Scale bar, 50 m. (F) NF200+ intrapancreatic ganglia per cubic millimeter. (G) Intrapancreatic ganglia volume. Ganglia per group: 123/43/80. (H) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 123/43/80. (I) cells contacting nerves per islet. Islets per group: 69/40/29. Data are shown as means SEM or median 95% confidence interval as indicated. Analyses by Kruskal-Wallis test with Dunns test (B to D) or unpaired t test (F to I), ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5 (A to D), N = 3 (F to H), and N = 4 (I).

To test the hypothesis that innervated islets differ from those without innervation, we then analyzed islet volume based on whether islets were innervated by NF200+ fibers, hypothesizing that neural signals may play a role in determining islet size. NF200-innervated islets were 10-fold larger than islets without NF200 innervation (Fig. 2B and fig. S1B), and as a result, innervated islets made up 43% of the total cell volume in the pancreas (Fig. 2C). Both innervated and noninnervated islets in the splenic region were larger than those in the duodenal pancreas (Fig. 2B).

Next, we analyzed the intensity of NF200+ immunostaining within each islet. NF200 protein levels are associated with structural stability of nerves and increased conduction velocity, so NF200+ immunostaining intensity may have functional correlates (29, 30). While the largest islets were more likely to be innervated, the intensity of NF200+ immunostaining was twofold greater in the smallest islets compared to the largest islets and greater in subpopulations of duodenal islets (Fig. 2D).

These data demonstrate that innervated islets are a small fraction of the total islet number but are significantly larger than islets without NF200 innervation and form a substantial portion of the total cell volume. These findings suggest the potential involvement of NF200+ nerves in islet development and cell growth.

Intrapancreatic ganglia integrate inputs from the sympathetic and parasympathetic nervous systems and provide significant islet innervation (31). Regional differences in ganglia size in the pancreas have been reported (32). Intrapancreatic ganglia are sparse (21.5 2.5 ganglia/mm3; Fig. 2, E and F), with an average volume of 83,467 10,646 m3 (Fig. 2G) and located close to islets (47.3 5.7 m; Fig. 2H). There were no significant regional differences in ganglia density, size, or location.

To assess whether islet innervation could directly influence endocrine cell function through neural signals, we quantified the number of cells contacting NF200+ nerves. Only 9.4 2.2% of cells contacted NF200+ nerves (Fig. 2I) with no regional difference. As expected, a larger number of cells contacted nerves in large islets compared to small islets (fig. S1D), but the proportion of cells contacting NF200+ nerves did not differ with islet size (fig. S1E). In aggregate, these data provide a comprehensive 3D atlas of the anatomy and NF200+ innervation of the entire mouse endocrine and exocrine pancreas that can be used as a benchmark to assess the effects of specific pancreatic innervation during development and in disease.

The 3D relationships between islets and innervation across the whole endocrine pancreas are largely unknown in diabetes. Hence, we determined how pancreatic anatomy and cell innervation were affected in a mouse model of type 1 diabetes (T1D). NOD mice provide a model of diabetes with autoimmune cell destruction and spontaneous T1D development. We examined the 3D structure of NF200+ innervation and islets in nondiabetic NOD mice (average nonfasting blood glucose, 115 4 mg/dl) and diabetic NOD mice (average nonfasting blood glucose, 495 62 mg/dl; Fig. 3A and movies S7 and S8).

(A) Pancreata from nondiabetic and diabetic NOD mice [maximum projection at 1.3 (top), 4 (middle), and 12 (bottom); scale bars, 2000, 500, and 200 m]. (B) cell volume. (C) Insulin+ islets per cubic millimeter of pancreatic tissue. (D) Insulin intensity (normalized against total pancreas, nondiabetic). (E) Insulin+ islet volume distribution (left axis) and median volume (right axis). Islets per group: 11,404/6285/5119/4057/2203/1854. (F) Exocrine nerve volume. (G) Endocrine nerve volume per insulin+ islet. (H) Endocrine nerve volume by islet volume. (I) Distribution of insulin+ islets located <1.6 and >1.6 m from the nearest nerve. (J) Mean insulin+ islet volume NF200+ innervation. Islets per group: 8815/857/4296/436. (K) Total insulin+ islet volume NF200+ innervation. (L) NF200 intensity sum normalized for insulin+ islet volume. Islets per group: 4941/3341/1209/383/2862/1586/189/73. (M) Intrapancreatic ganglia per cubic millimeter. (N) Intrapancreatic ganglia volume. Ganglia per group: 112/82. (O) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 111/54. (P) cells contacting nerves per islet. Islets per group: 28/14. Data are shown as means SEM or median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test (B to D and F to G), Kruskal-Wallis with Dunns test (H and J to L), or unpaired t test between diabetic and nondiabetic groups (H). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 7 nondiabetic and N = 7 diabetic (B to E, P); N = 8 nondiabetic and N = 7 diabetic (F to L); N = 6 nondiabetic and N = 6 diabetic (M to O).

Across the whole pancreas, islet density and cell volume in female nondiabetic NOD mice were similar to that seen in male C57BL/6 mice (Figs. 1, D and E, and 3, A to C). In female diabetic NOD mice, the cell volume was significantly lower across the whole pancreas, reduced to 10% of the volume in nondiabetic NOD mice in both splenic and duodenal regions (Fig. 3B). The islet number was also significantly reduced in diabetic NOD mice, particularly in the splenic, but not duodenal pancreas (Fig. 3C). However, the intensity of insulin immunostaining was preserved in the remaining islets that were detected in diabetic NOD mice (Fig. 3D). There was a significant inverse correlation between blood glucose levels and both islet number and cell volume (fig. S2A).

The volume distribution of insulin+ islets in nondiabetic NOD mice was also comparable to C57BL/6 mice (Fig. 3E). However, islet volume distribution was significantly shifted in diabetic NOD mice, with marked loss of larger islets. Insulin+ islets over 50,000 m3 were reduced by more than half, and the median islet volume decreased by more than 50%. The loss of large islets was particularly notable in the duodenal pancreas (Fig. 3E).

Together, these data demonstrate marked decreases in insulin+ islet number and volume and marked alterations in islet volume distribution in diabetic compared to nondiabetic NOD mice, particularly in the duodenal pancreas. Our data also suggest that the remaining islets in diabetic NOD mice maintained their insulin content.

Previous studies have reported alterations in pancreatic innervation in mouse models of diabetes (13, 3336). Therefore, we examined pancreatic innervation in NOD mice to determine effects on nerve density in the different regions of the pancreas (movies S9 and S10).

Nerve density in insulin+ islets was increased more than twofold in diabetic NOD mice (Fig. 3, G and H), particularly in the splenic pancreas. Islet nerve density in the splenic pancreas positively correlated with blood glucose (fig. S2C). The regional differences in endocrine nerve density observed in C57BL/6 mice were absent in nondiabetic NOD mice. There was no difference in exocrine nerve density between nondiabetic and diabetic NOD mice and no correlation with blood glucose (Fig. 3F and fig. S2B).

Previous studies suggest that neural signals contribute to cell survival (37), so increased islet innervation could result from differences in the susceptibility of innervated and noninnervated islets to immune destruction. To test this, we examined the proportion of NF200+ islets (islets containing or in contact with NF200+ fibers) in NOD mice. We did not see any significant change in the proportion of NF200+ islets (14.6 versus 9.8% islets in nondiabetic and diabetic NOD mice, respectively; Fig. 3I). However, the proportion of NF200+ islets was increased in a subset of islets with volumes between 50,000 and 500,000 m3 in diabetic NOD mice (fig. S2F). The median distance between islets and nerves was similar in diabetic and nondiabetic NOD mice for the total pancreas but significantly reduced in the splenic pancreas (fig. S2D).

In keeping with the results in C57BL/6 mice, NF200+ islets were significantly larger than NF200 islets in both diabetic and nondiabetic NOD mice (Fig. 3J), although, as expected, the average volume of both NF200 and NF200+ islets decreased in diabetic NOD mice. Innervated insulin+ islets remained 60% of the total cell volume in both diabetic and nondiabetic NOD mice (Fig. 3K).

In published studies, the intensity of NF200 immunostaining decreases with nerve damage and increases in nerve regeneration (20, 22). To indirectly assess the effects of autoimmune diabetes on nerve integrity in islets, we examined the intensity of NF200 immunostaining in diabetic and nondiabetic NOD mice and found that the intensity of NF200 immunostaining was significantly increased in islets from diabetic NOD mice (Fig. 3L).

We next examined intrapancreatic ganglia to determine whether autoimmune diabetes altered their distribution or size. There was no significant difference in intrapancreatic ganglia density (18.9 5.2 versus 28.2 10.1 ganglia/mm3, nondiabetic versus diabetic NOD mice, respectively; Fig. 3M) or volume (61,779 5961 versus 59,348 6977 m3, nondiabetic versus diabetic NOD mice, respectively; Fig. 3N), but the distance between intrapancreatic ganglia and islets increased fourfold in diabetic NOD mice (40 5.3 versus 171.7 17.6 m, nondiabetic versus diabetic, respectively; Fig. 3O).

Next, we examined the proportion of cells in contact with NF200+ fibers in nondiabetic and diabetic NOD mice. Despite a significant increase in islet nerve density, there was no significant change in the proportion of cells contacting nerves in diabetic NOD mice (Fig. 3P).

Autoimmune cell destruction principally affects cells in NOD mice resulting in islets composed primarily of glucagon+ cells. The changes in cell innervation in mouse models of diabetes are largely unknown. In diabetic NOD mice, glucagon staining is clearly present, but glucagon+ cells from a single islet may form several clusters rather than a clearly defined, single islet (Fig. 4, A and B). As previously reported (38), the ratio of glucagon to insulin volume (Fig. 4C) was significantly increased in diabetic NOD mice (movies S11 and S12). In nondiabetic NOD mice, NF200 nerve density in cell clusters was markedly higher than nerve density in insulin+ islets. Nerve density in diabetic NOD mice was unchanged (Fig. 4D). The proportion of innervated cell clusters was similar to that of innervated insulin+ islets in nondiabetic NOD mice and increased twofold in diabetic NOD mice (Fig. 4E). In keeping with increased NF200 nerve density in cell clusters of nondiabetic NOD mice, the proportion of cells contacting NF200+ fibers was more than fivefold higher than cells contacting NF200+ fibers in nondiabetic NOD mice. However, the proportion of cell nerve contacts did not change in diabetic mice (Fig. 4F).

(A) Maximum projections of light-sheet images of pancreatic samples from nondiabetic and diabetic NOD mice stained for insulin (green), NF200 (magenta), and glucagon (blue) and imaged at 4 magnification. Scale bars, 200 m. (B) cell volume corrected for pancreatic volume in NOD mice. (C) Glucagon+ cell volume as a percentage of insulin+ cell volume in NOD mice. (D) NF200+ nerve volume within glucagon+ cell clusters in NOD mice. (E) Glucagon+ cell cluster volume (left axis) and median nerve distance (right axis) in NOD mice. (F) Percentage of cells contacting nerves per islet. Number of islets: 23/16. Data are shown as mean SEM or as median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test (D) or Kruskal-Wallis with Dunns test (B, C, and E). *P < 0.05. T, total; D, duodenal; S, splenic. N = 3 nondiabetic and N = 3 diabetic NOD mice.

In summary, insulin+ islet nerve density and NF200 immunostaining are increased in the surviving insulin+ islets of diabetic NOD mice, and cell contacts with NF200+ fibers are preserved. cell nerve density and cell contacts with NF200+ fibers are greater than contacts with cells, and cell nerve density also increases in diabetic NOD mice.

On the basis of our findings in NOD mice, we hypothesized that nerve density may progressively increase in surviving islets during the development of diabetes. To test this hypothesis, we examined the time course of changes in insulin+ islets and pancreatic nerves in mice with STZ-induced diabetes, as well as in age- and sex-matched C57BL/6 mice. Diabetes secondary to multiple low-dose STZ treatment is likely induced by both direct cell toxicity and islet inflammation. Therefore, using a standard 5-day low-dose STZ model, we examined NF200, insulin, and glucagon staining in mice sacrificed 5 and 15 days after completion of STZ treatment (nonfasting blood glucose: 259 18 and 430 17 mg/dl, respectively) and compared these to untreated littermate controls (nonfasting blood glucose: 123 9 mg/dl; Fig. 5A and movies S13 and S14).

(A) Pancreata at days 5 (left) and 15 (right) after STZ treatment, maximum projections at 1.3 (top), 4 (middle), and 12 (bottom). Scale bars: 1000, 500, and 200 m. (B) cell volume. (C) Insulin+ islets per cubic millimeter. (D) Insulin intensity (normalized against total pancreas, control). (E) Insulin+ islet volume distribution (left axis) and median volume (right axis). Islets per group: 10,479/4682/5797/10,091/5162/4929/14,380/7543/6837. (F) Exocrine nerve volume. (G) Endocrine nerve volume per insulin+ islet. (H) Endocrine nerve by islet volume. Data are shown as mean SEM or as median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test for comparison between control, STZ day 5, and STZ day 15, and unpaired t test for comparison between duodenal and splenic pancreas (B to D and F to H) or Kruskal-Wallis with Dunns test (E); *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5 control, N = 6 STZ day 5, and N = 7 STZ day 15 (B to E); N = 6 control, N = 5 STZ day 5, and N = 5 STZ day 15 (F to H).

First, we analyzed islet number and total cell volume to determine the time course and effects of STZ treatment, hypothesizing that STZ may differentially affect these parameters in different pancreatic regions. As expected in this model, total cell volume was reduced to 40% of control, and intensity of insulin immunostaining was decreased by 50% with STZ treatment (Fig. 5, B to D). Islet number and cell volume negatively correlated with blood glucose in STZ-induced diabetes (fig. S3A). STZ treatment did not significantly alter the distribution of cell volumes throughout the pancreas, suggesting that its effects were uniform across islets of all sizes (Fig. 5E). However, there was a significant decrease in the individual islet volume, first in the duodenal pancreas at day 5 and later at day 15 in both the duodenal and splenic regions. These findings demonstrate that STZ treatment progressively reduces total cell volume, intensity of insulin immunostaining, and the volume of individual islets, with significant differences in the time course and extent of these changes between duodenal and splenic pancreas.

We next examined pancreatic innervation in STZ-treated mice to determine the time course and regional distribution of effects on nerve density (movies S15 and S16). Nerve density in the exocrine pancreas was significantly increased 15 days after STZ treatment (Fig. 5F and fig. S3B). STZ treatment significantly increased islet innervation and islet nerve density by twofold on day 5 (Fig. 5, G and H). Islet nerve density was significantly correlated with blood glucose (fig. S3A).

To test the hypothesis that neural signals may play a role in cell preservation, we assessed whether STZ treatment had differential effects on islets based on whether they contained NF200+ nerves or not. STZ treatment led to a progressive increase in the proportion of NF200+ islets across the duodenal and splenic pancreas (Fig. 6A) and all islet sizes (fig. S3F) but did not reach significance (P = 0.14). STZ treatment significantly reduced the distance between insulin+ islets and NF200+ fibers on day 5, primarily in the splenic pancreas (fig. S3D). In both control and STZ-treated mice, innervated islets are significantly larger than noninnervated islets but decline in volume with STZ treatment (Fig. 6B). The total volume of innervated islets, but not of noninnervated islets, significantly decreased with STZ treatment (Fig. 6C) but remained 54% of the remaining total cell volume.

(A) Distribution of insulin+ islets located <1.6 and >1.6 m from the nearest nerve. (B) Mean volume for insulin+ islets NF200+ innervation. Islets per group: 10,199/929/5300/366/9837/1022. (C) Total volume for insulin+ islets NF200+ innervation. (D) Intensity of NF200 immunolabeling normalized for insulin+ islet volume. Islets per group: 3013/2762/1837/605/2842/1791/1057/336/5800/3274/1500/386. (E) Ganglia per cubic millimeter. (F) Volume of intrapancreatic ganglia. Ganglia per group: 114/73/97. (G) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 114/73/97. (H) Percentage of cells contacting nerves per islet. Islets per group: 69/28/69. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by Kruskal-Wallis with Dunns test (B to D) for comparison between control, STZ day 5, and STZ day 15, and unpaired t test for comparison between duodenal and splenic pancreas (E to H). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 6 control, N = 5 STZ day 5, and N = 5 STZ day 15 (A to D); N = 6 control, N = 3 STZ day 5, and N = 3 STZ day 15 (E to H).

To determine whether STZ-induced diabetes modified the expression of NF200, we assessed changes in intensity of NF200 immunostaining in relation to insulin+ islet volume and time after treatment (Fig. 6D). The intensity of NF200 immunostaining (corrected for cell volume) was significantly increased in the largest islets (>500,000 m3) 5 days after STZ treatment and by twofold to fourfold in all islets at 15 days after STZ treatment. These findings demonstrate that STZ treatment increases exocrine and endocrine nerve density and NF200 expression, results that are in keeping with increased nerve growth.

We next examined intrapancreatic ganglia in mice treated with STZ to determine whether cell destruction changed their density or size. While STZ treatment did not change intrapancreatic ganglion density or distance from the islet, there was a 30% decrease in ganglion volume 15 days after STZ treatment (Fig. 6, E to G). Similar to our findings in NOD mice, although islet nerve density increased with STZ treatment, the proportion of cells contacting NF200+ fibers did not change significantly (Fig. 6H).

We next assessed cell volume and nerve density in cell clusters in STZ-treated mice. STZ treatment increased the ratio of glucagon+ to insulin+ cell volume, but total glucagon+ cell volume was reduced after 15 days (Fig. 7, A to C, and movie S17). NF200 nerve density in cell clusters was significantly increased in STZ-treated mice (Fig. 7D), but the proportion of innervated cell clusters did not change (Fig. 7E). Similarly, the proportion of cells contacting NF200+ fibers was not significantly altered by STZ treatment (Fig. 7F).

(A) Pancreata at days 5 (left) and 15 (right) after STZ treatment. Insulin, green; NF200, magenta; glucagon, blue. Imaged at 4 magnification. Scale bars, 200 m. (B) Quantification of cell volume corrected for pancreatic volume in STZ-treated mice. (C) Quantification of glucagon+ cell volume as a percentage of insulin+ cell volume in STZ-treated mice. (D) Quantification of NF200+ nerve volume within glucagon+ cell clusters in STZ-treated mice. (E) Glucagon+ cell cluster volume (left axis) and median nerve distance (right axis) in STZ-treated mice. (F) Percentage of cells contacting nerves per islet. Islet number: 98/37/53. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by one-way ANOVA with Tukeys test (C to D) or Kruskal-Wallis with Dunns test (B and E). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 6 control, N = 4 STZ day 5, and N = 6 STZ day 15.

In summary, 3D representation faithfully represents the progressive reduction in islet number, cell volume, and intensity of insulin immunostaining in response to STZ treatment. Further, STZ treatment increases insulin+ islet nerve density, the proportion of innervated islets, and intensity of NF200 immunostaining. cell nerve density is increased with STZ treatment, but the proportion of and cells that are in contact with NF200+ fibers is not significantly altered in STZ-treated mice.

Islet innervation differs between species (3, 39) and the 3D relationships between islets and pancreatic nerves in healthy versus diabetic patients remain largely unknown. To assess these, islets and NF200+ innervation were examined in small, cleared pancreatic samples from healthy human donors and donors with type 2 diabetes (T2D; Table 1) by light-sheet imaging to assess islet distribution and relationship to innervation (Fig. 8A and movies S18 and S19).

CVA, cardiovascular accident; HbA1c, hemoglobin A1C; N/A, not applicable; PFA, paraformaldehyde; M, male; F, female.

(A) Maximum projections of pancreatic samples from human donors without (C1 to C5) and with type 2 diabetes (DM2; D1 to D3) at 1.3. Scale bars, 1000 m. (B) cell volume. (C) Insulin+ islets per cubic millimeter. (D) Insulin+ islet volume distribution (left axis) and median volume (right axis). (E) Exocrine nerve volume. (F) Endocrine nerve volume per insulin+ islet. (G) Endocrine nerve volume corrected for insulin+ islet volume. (H) Distribution of insulin+ islets located at <1.6 and >1.6 m from nerves. Islets per group: 28,315 control and 6790 DM2. (I) Mean volume of insulin+ islets NF200+ innervation. Islets per group: 25,519/236/7448/345. (J) Total volume of insulin+ islets NF200+ innervation. (K) Intrapancreatic ganglia (NF200, magenta; confocal, 20). Boxed areas magnified in lower panels with cell bodies indicated by arrows. Scale bars, 50 m (top) and 25 m (bottom). (L) Ganglia per cubic millimeter. (M) Volume of intrapancreatic ganglia. Ganglia per group: 31/12. (N) Distance between intrapancreatic ganglia and insulin+ islets. Ganglia per group: 31/12. (O) cells contacting nerves per islet. Islets per group: 73/28. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by unpaired t test (B to G and L to M), Mann-Whitney test (H), or Kruskal-Wallis with Dunns test (I to J). *P < 0.05, **P < 0.01, and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5 control and N = 3 DM2.

As expected, total cell volume (Fig. 8B) and islet number (Fig. 8C) were highly variable (40). The cell volume (as a percentage of the total pancreatic sample volume) was lower in the diabetic donors, varying between 0.47 and 2.2% in the control group and 0.85 and 0.97% in the diabetic group. Islet numbers ranged from 66 to 200 islets/mm3 in the control group to 58 to 287 islets/mm3 in the diabetic group. While islet number per cubic millimeter was greater in humans than in mice, the cell volume (%) was very similar in murine and human tissues. The cell volume distribution in nondiabetic pancreata was not significantly different to that in mice (Fig. 8D). Larger islets were disproportionately reduced, and the mean volume of an individual islet was significantly lower in diabetic compared to healthy individuals.

In human samples from healthy donors, NF200+ innervation was similar between endocrine (0 to 0.89% nerve volume per islet) and exocrine tissue (0.06% to 0.94% nerve volume per exocrine tissue; Fig. 8, E to G). Exocrine nerve volume, endocrine nerve volume per islet, islet nerve density, and proportion of innervated islets were greater in diabetic individuals (Fig. 8H). In nondiabetic individuals, innervated insulin+ islets are significantly larger than those without innervation, in line with the findings in mice, but innervated insulin+ islets are a smaller proportion of the total islet volume than seen in mouse pancreata. In diabetic individuals, innervated islets are significantly smaller than in nondiabetic individuals (Fig. 8, I and J).

We next assessed intrapancreatic ganglia in human pancreatic samples. Human intrapancreatic ganglia were larger than those found in mice (Fig. 8K), but ganglion density and distance from islets were similar to C57BL/6 and nondiabetic NOD mice. There was no significant difference in ganglia size between nondiabetic and diabetic donors (Fig. 8, L to N). Last, we examined contacts between NF200+ nerves and cells. The proportion of cells in contact with NF200+ fibers was half of that in control mice (4.15 versus 9.44%) and was preserved in individuals with diabetes (6.07%; Fig. 8O).

Together, cell volume and distribution in human pancreata were comparable to murine pancreata, and innervated islets were significantly larger than noninnervated islets. In samples from individuals with diabetes, exocrine and endocrine innervation as well as proportion of innervated islets were increased, and nerve contacts with cells persist.

The pancreas is composed of multiple cell types, is richly vascularized, and is densely innervated by sympathetic, parasympathetic, and sensory nerves. We wanted to compare our analyses of pancreatic innervation using the pan-neuronal marker, NF200, with pathway-specific pancreatic innervation. Using a modified iDISCO+ protocol specifically optimized for pancreatic tissue, we examined tyrosine hydroxylase (TH) immunolabeling to mark sympathetic nerve fibers (movie S20) and vesicular acetylcholine transporter (VAChT) immunolabeling to identify parasympathetic nerve fibers (movie S21) across the mouse pancreas. There was more TH+ (Fig. 9, A to D) and VAChT+ (Fig. 9, H to K) innervation than NF200+ innervation in both the exocrine and endocrine pancreas. In keeping with our findings examining NF200+ fibers, TH and VAChT nerve density were threefold to more than sixfold greater in the endocrine than in the exocrine pancreas. The proportion of islets containing or in contact with TH+ fibers was 27.7% (Fig. 9E) compared to 35.0% for VAChT+ fibers (Fig. 9L). In line with our findings with NF200, both TH (Fig. 9F) and VAChT (Fig. 7M) innervated islets were significantly larger than noninnervated islets, and as a result, the majority of insulin+ islet volume is composed of innervated islets (Fig. 9, G and N).

(A) Maximum projections of TH and insulin. Scale bars, 2000 m at 1.3 and 200 m at 4. (B) TH+ exocrine nerve volume. (C) TH+ endocrine nerve volume per insulin+ islet. (D) TH+ endocrine nerve volume by insulin+ islet volume. (E) Distribution of insulin+ islets located at <1.6 and >1.6 m from TH+ nerves. Islets per group: 10,610/4773/5837. (F) Mean insulin+ islet volume for insulin+ islets with and without TH+ innervation. Islets per group: 8542/3314/4699/1320/3843/1994. (G) Total volume for insulin+ islets with and without TH+ innervation. (H) Maximum projections of VAChT and insulin. Scale bars, 2000 m at 1.3 and 200 m at 4. (I) VAChT+ exocrine nerve volume. (J) VAChT+ endocrine nerve volume per insulin+ islet. (K) VAChT+ endocrine nerve volume corrected for insulin+ islet volume. (L) Distribution of insulin+ islets located at <1.6 and >1.6 m from VAChT+ nerves. Islets per group: 12,661/7165/5496. (M) Mean volume for insulin+ islets with and without VAChT+ innervation. Islets per group: 8542/3314/4699/1320/3843/1994. (N) Total volume for insulin+ islets with and without VAChT+ innervation. Data are shown as means SEM or as median 95% confidence interval where indicated. Analyses by unpaired t test (B to E and I to L) or Kruskal-Wallis with Dunns test (F, G, M, and N). **P < 0.01 and ***P < 0.001. T, total; D, duodenal; S, splenic. N = 5.

The optimized iDISCO+ protocol was also effective for labeling fibers expressing TRPV1 (transient receptor potential cation channel, subfamily V, member 1) and synapsin (fig. S4). In addition, we applied a novel alternative approach to visualize islet vasculature by combining insulin immunolabeling with fluorophore-tagged dextran or CD31 antibody, followed by optical clearing using ethyl cinnamate (ECi) to preserve fluorescence while allowing for additional immunostaining tissue (fig. S4) (41). These data demonstrate that modification of optical clearing protocols allows for visualization of multiple markers in pancreatic tissue.

In summary, NF200, TH, and VAChT immunostaining all demonstrate that large islets have enriched islet innervation and that large innervated islets represent at least half of total pancreatic islet volume. However, analysis of pancreatic innervation using TH and VAChT immunostaining suggests that endocrine nerve density is greater than revealed by NF200 immunostaining.

Tissue clearing, 3D imaging, and unbiased image analysis have been widely used in the CNS to provide new insights into anatomical pathways and patterns of regional activation. However, there have been few applications in peripheral organs such as the pancreas. Whole-organ clearing and imaging are especially suited for the mapping of filamentous structures, particularly to delineating innervation across large distances that can be difficult to achieve using traditional serial sections and 2D imaging. Tissue clearing has been used previously in thick pancreatic sections (350 to 1000 m) (9, 10, 12, 4244) and small pieces of pancreatic tissue (11) and then imaged with optometry or high-magnification confocal microscopy for detailed analysis [see (4547) for review]. This has provided important information about islet characteristics and structural relationships over a close range. In particular, previous studies using 3D imaging in pancreatic sections, fetal tissue, and young mice have provided data about islet innervation (9, 10, 13, 14, 42, 48). Our data extend these important published studies. Different pancreatic regions have diverse embryological origins and variations in islet density and function and are supplied by neurons from different extrapancreatic ganglia. Without assessing pancreatic structure across the whole organ, our understanding and quantification of pancreatic anatomy, including possible regional differences, are incomplete and possibly inaccurate.

Tissue clearing and volume imaging of the pancreas provided several new insights. Innervation of the endocrine pancreas is significantly enriched compared to the surrounding exocrine pancreas, with marked regional variation. Islets are closely associated with pancreatic innervation, and innervated islets are significantly larger than noninnervated islets, in both mouse and human. Intrapancreatic ganglia are sparse and close to islets. Almost half of cells and a tenth of cells contact NF200+ fibers, irrespective of islet size or location. Last, islet nerve density and expression of NF200 are increased in the remaining islets of two mouse models of T1D, with temporal and regional differences, and greater in human T2D, in keeping with nerve remodeling.

3D imaging across the whole pancreas provides straightforward measurement of multiple islet characteristics and identifies significant regional differences that would be laborious or impossible to obtain by serial sectioning. We readily measured cell volume across multiple pancreata, and our findings are in agreement with previous studies at 1 to 2% in mouse pancreas (24) and at 1 to 4% in human pancreas (49, 50). Increased islet volume in the splenic pancreas is in keeping with previous observations using 2D histology, in isolated islets and in transgenic mice (51, 52). Intensity of insulin immunostaining was significantly lower in the splenic pancreas. The splenic pancreas contains significantly larger islets, and previous studies report lower insulin immunostaining intensity and fewer insulin granules in large islets, while other studies demonstrate lower c-peptide content in cells from the splenic pancreas (53, 54). Our approach also facilitates rapid analysis of islet volume distribution across the pancreas. The majority of islets were between 1000 and 500,000 m3, equivalent to islet diameters of 12 to 98 m (assuming spherical islets), with around a fifth of islets having volumes larger than 500,000 m3. Whole-organ imaging demonstrated significant differences in islet biology between diabetes models. Islet number and cell volume were reduced in diabetic NOD mice, with the intensity of insulin immunostaining relatively preserved in some islets and a notable shift to small islets. Islet number and volume were also reduced with STZ treatment, but intensity of insulin immunostaining was markedly reduced, and size distribution was minimally altered. Together, these observations validate tissue clearing and 3D imaging as a reliable straightforward method to assess cell volume and other characteristics across the entire pancreas.

Whole-tissue 3D imaging confirmed dense pancreatic innervation and revealed markedly greater nerve density in the endocrine pancreas, over sixfold greater than in the exocrine pancreas of mice. These findings confirm the results of previous studies reporting close association between islets and nerves using 2D histology and 3D examination of pancreatic sections (3, 9, 10). We extended these findings to show that endocrine NF200+ innervation was not uniform throughout the pancreas but enriched in the duodenal portion. These regional differences may reflect the distinct embryological origins of the duodenal and splenic regions. Further studies will determine whether regional differences in pancreatic innervation contribute to reported regional differences in islet composition, size, function, and susceptibility to immune loss.

3D analysis of islets and innervation across the whole pancreas revealed previously unknown features of the close anatomical relationship between islets and nerves. In mice and human samples, innervated islets are a relatively small fraction of all islets by number, but they are, on average, 10-fold larger than noninnervated islets. As a result, innervated islets represent around half of the total cell volume. The large volume of innervated islets is in accordance with a role for neural signals in islet development and maintenance. In both mice and zebrafish, cells aggregate close to pancreatic nerves in development, and islet architecture is disrupted by loss of neural signals (55, 56). There is also close physical association between nerves and islets in human embryos, particularly in the middle and late trimester, when there is rapid development of the endocrine pancreas (55). Less is known about the role of neural signals in cell maintenance, but vagotomy reduces cell replication in rats (57). Vagotomy disrupts the afferent and efferent signals to several intra-abdominal organs, so further studies are needed to determine whether loss of neural signals, specifically to the pancreas, disrupts islet structure and function during development and after birth.

Islets are closely associated with the pancreatic duct and highly vascularized, so it is possible that the proximity between nerves and islets is related to innervation of the duct or vessels. Islet blood vessels are richly innervated, and neural signals have marked effects on islet blood flow (58). However, several studies suggest that vascular signals actually reduce endocrine cell differentiation during development, and in zebrafish, islets remain densely innervated even in the absence of islet vascularization.

Although NF200-innervated islets are large, on average, NF200 immunostaining intensity is greater in smaller innervated islets. NF200 expression has been linked to nerve diameter and conduction velocity, so it is possible that differences in NF200 immunostaining intensity may have functional consequences (29, 30). In human pancreata, small islets have a greater proportion of cells compared to other endocrine cell types and higher insulin content (59). Similarly, small rat islets were functionally superior to larger islets in ex vivo studies and after transplantation (60). In keeping with previous work (3), NF200+ nerves contact a small proportion of cells in each islet, with no proportional differences between pancreatic regions or islet sizes. The proportion of innervated cells is similar to the percentage of cells that are reported to act as hub cells in the islets, and hub cells are reported to be modulated by cholinergic agonists (61). Although a minority of cells contact NF200+ nerves, neural signals could influence activity across multiple cells through electrical coupling. In contrast, and in keeping with previous work (3), NF200+ nerves contact a much greater proportion of cells, which lack gap junctions. Whether variation in NF200 immunostaining intensity or proximity of individual cells to nerves contributes to functional heterogeneity of cells is currently unknown and warrants further studies.

The development of diabetes in NOD mice and in STZ-treated mice is associated with rapid and significant increases in islet nerve density. In NOD mice, increased nerve/islet volume suggests that nerve volume may be preserved, while cell volume is reduced in surviving islets. The proportion of innervated cell clusters increased in diabetic NOD mice. In STZ-treated mice, nerve volume per islet, nerve density per islet, and nerve density in cell clusters are all increased. These findings suggest that increased nerve density is restricted to the remaining insulin+ islets in NOD mice, while both cell and cell nerve density is increased in STZ-treated mice. The increases in insulin+ islet nerve density in diabetic NOD and STZ-treated mice are similar in magnitude to nerve density changes in response to physiologically relevant stimuli. In the CNS, fasting leads to an almost twofold increase in agouti related neuropeptide (AgRP)/neuropeptide Ypositive (NPY+) terminals in the paraventricular nucleus, a major pathway regulating food intake (62). In the peripheral nervous system, skin inflammation increased sensory nerve density twofold and was associated with increased sensitivity to thermal and mechanical stimuli (63). Further studies will be required to test the functional consequences of increased islet nerve density.

The intensity of NF200 immunostaining was significantly increased in the islets of diabetic NOD and STZ-treated mice. NF200 staining intensity increases in response to nerve regeneration (20, 21), so up-regulation of NF200 may reflect ongoing regeneration of islet innervation. These findings, and the time course of the increased nerve density with STZ treatment, are highly suggestive of nerve regeneration; reported rates of nerve regrowth after crush injury are up to 4 mm/day, and restoration of electrical activity in peripheral nerves after chemical injury occurs within days. Our findings may be responses to STZ directly, to hyperglycemia, and to inflammatory processes and/or interactions between endocrine cells and neurons to regulate neural density. Increased nerve density is reported in response to inflammation in several tissues and their adjacent structures, and these changes can either be protective or exacerbate inflammation (64, 65). Increased insulin+ islet nerve density in NOD mice, and the increase in exocrine and endocrine nerve density in STZ-treated mice, may reflect the major sites of immune activity/inflammation. Both hyperglycemia and STZ treatment increase cell production of nerve growth factor (NGF) (66). Its receptor, tyrosine kinase receptor A (TrkA), is expressed on both sympathetic and sensory nerves, and previous 2D imaging studies report that sensory and sympathetic nerve density are increased with STZ treatment (33). In previous studies, NGF overexpression in cells significantly increased sympathetic islet innervation (67). The cross-talk between nerves and islets in healthy versus diabetic tissue remains largely unstudied.

There is considerable variability in the reported changes in cell mass in models of diabetes. In our studies in diabetic NOD mice, cell volume was not significantly higher as a proportion of total pancreatic volume, but the ratio of to cell volumes was significantly increased (Fig. 4, B and C). Similar to our findings, Plesner et al. (68) describe a non-significant increase in cell mass in prediabetic and diabetic NOD mice and a significant increase in the proportion of cells/islet area in diabetic NOD mice. Our longitudinal studies show changes in cell volume with time after STZ treatment, with a significant decrease 2 weeks after the completion of treatment. This is in keeping with previous studies (69), but the cell response to STZ treatment has also been described to increase (70), to remain unchanged (71), or to vary with time after STZ treatment (72).

In our studies, and cell contacts are maintained in diabetic NOD and STZ-treated mice. One limitation of our assessment is that we quantify the number of endocrine cells contacting nerves, but we cannot quantify the number of contacts per endocrine cell. It is possible that the number of contacts with or cells is modified in diabetes. Sympathetic fibers also contact delta cells and vasculature to a lesser extent (3) in mouse islets. Further work is needed to determine whether NF200+ fibers contact delta cells or other islet structures and the effects of diabetes and STZ treatment on these contacts.

Our results examining innervation in NOD mice are consistent with recent 3D imaging of thick pancreatic sections (9, 13) reporting regions with increased innervation in these mice. Prior 2D studies have reported loss of islet sympathetic innervation in NOD mice (35), but these studies used different neural markers and examined NOD mice with longer duration of diabetes. It is possible that the increased islet innervation in NOD mice we observe is lost with increasing duration of diabetes. Alternatively, there may be pathway-specific changes such that sympathetic innervation is reduced, but parasympathetic and/or sensory innervation are increased, leading to an increase in NF200+ innervation found in our studies. Future studies using iDISCO+ will be required to dissect the longitudinal changes and contribution of specific neural pathways in mouse models of T1D, as well as the associations between innervation and immune infiltration.

There are several differences between mouse and human islets as well as similarities between these species. Human cell volume (%) was similar to the proportion of insulin+ islets in C57BL/6 mice. Islet size distribution was also remarkably similar between mouse and human pancreata. Similar to the findings in mice, intrapancreatic ganglia in human tissue are sparse and close to islets, but they are markedly larger, of the order of 200 neurons on average. There have been conflicting results about islet innervation in human versus murine samples. In our studies, the proportion of innervated islets and the finding that innervated islets were larger than noninnervated were similar in both humans and mice. Initial 2D imaging reported reduced islet innervation in human samples, but recent data from optically cleared human samples using markers for sympathetic nerves suggest that human islets, like mouse islets, have a dense neural network (9, 11). In keeping with previous studies examining TH+ fibers in human pancreatic samples (3, 11), innervation density is similar in human exocrine and endocrine pancreas. We found that NF200+ innervation is present in human islets, in keeping with previous studies demonstrating TH+ fibers in islets (3, 11), but at a lower density than mouse islets. A smaller proportion of human cells contact NF200+ fibers compared to mouse islets.

There are also similarities between innervation in STZ-treated mice and in samples from T2D individuals. In pancreata from T2D donors, nerve volume per islet, nerve density, and the proportion of innervated islets are all increased. The human tissue samples we analyzed provide a snapshot of islets and innervation from postfixed tissue as well as from individuals with variable comorbidities, age, and time from death. These factors likely contribute to the sample variability, in line with previous human data (73). In aggregate, our data suggest that islet innervation is present in human islets, albeit at lower levels than mouse islets, and innervation appears to be at least preserved, possibly increased, in human T2D individuals. The increase in exocrine innervation in pancreatic tissue from T2D donors may reflect more generalized pancreatic pathology that is increasingly recognized as a feature of T2D. One limitation of our studies is that we did not examine insulin islets by glucagon staining in the human samples, so it is unknown whether whole islet nerve density or cell contacts are altered in T2D. Further studies examining specific neural pathways and further endocrine cell types in human pancreatic tissue are required to fully assess normo- and pathophysiological species differences.

Our studies using NF200 as a neural marker do not differentiate between parasympathetic, sympathetic, and sensory fibers. Using an optimized iDISCO+ protocol, we examined sympathetic and parasympathetic innervation in wild-type mice, and many findings mirror those seen with NF200+ innervation. Similar to our findings using NF200, both sympathetic and parasympathetic endocrine innervation are enriched compared to exocrine innervation, and innervated islets are significantly larger than noninnervated islets. These findings differ from published studies that show similar TH+ innervation density in exocrine and endocrine tissue (74). However, previous reports analyzed innervation in female mice sampling cryosections every 400 m rather than innervation across the whole pancreas. The proportion of innervated-to-noninnervated islets is also greater when assessed using sympathetic and parasympathetic markers. The distribution of TH+ and VAChT+ innervation differs, with several large volume TH+ fibers contributing to higher TH+ volume compared to VAChT+ innervation in the exocrine pancreas. In keeping with previous reports in adult mice (75, 76), we observed occasional TH+ cells. We excluded these, as far as possible, based on their morphology, volume, and overlap with insulin immunostaining, but it is conceivable that our estimate of TH+ innervation may be an overestimate. TH+ and VAChT+ endocrine innervation are higher than for NF200. However, while NF200 has been reported to be expressed in a wide range of myelinated and unmyelinated fibers, our results and previous studies suggest that NF200 does not label all fibers (19), and it is possible that we may have overlooked alterations in NF200 fibers in our studies. Alternative pan-neuronal markers have significant limitations. For example, protein gene product 9.5 (PGP9.5) is expressed in islet endocrine cells and innervation. Pathway-specific markers are also imperfect. TH labels most, but not all, sympathetic nerve fibers since there are also populations that are TH but express NPY (77). VAChT immunostaining is primarily in the terminal neuronal arborization and so visualizing larger cholinergic nerve fibers may be incomplete (78). While there are similarities among innervation patterns with NF200, TH, and VAChT, our studies do not allow us to determine whether the changes in pancreatic innervation with diabetes are generalized or specific to sympathetic, parasympathetic, or sensory pathways. Future work will assess important pathway-specific changes in pancreatic innervation and their contacts with specific endocrine cell types in both mouse and human metabolic disease. One disadvantage of iDISCO+ is that it does not preserve endogenous fluorescence. Therefore, we also developed and validated a novel alternative approach that combines immunostaining and tissue clearing with ECi for use in adult murine tissues (41). This preserves endogenous fluorescent signals while allowing for antibody labeling of additional targets. ECi clearing also provides a less toxic alternative to iDISCO+ (41). A combination of fluorescently tagged dextran to delineate blood vessels, immunostaining for innervation and islets, and ECi tissue clearing will allow us to further assess the organ-wide association between innervation, islets, and vasculature.

In summary, we have used whole-organ tissue clearing and imaging to create a 3D atlas mapping islets and innervation across the pancreas as a tool to quantify cell mass, define islet characteristics, map pancreatic innervation, and assess the anatomical interaction between islets and innervation in healthy and diabetic mice and humans. This approach demonstrates dense islet innervation and identifies distinguishing features of innervated islets and the regional differences. Such regional variations illustrate the importance of whole-organ imaging when assessing pancreatic anatomy. Our studies confirm that innervation is present in human islets and directly contacts cells. We demonstrate that islet innervation is markedly increased in diabetic NOD mice, STZ-treated mice, and likely in diabetic human pancreata. In combination with up-regulation of NF200 immunostaining, this suggests increased rapid reorganization of pancreatic innervation and possible nerve growth within islets. Future studies will identify the neurochemical characteristics, time course, and functional consequences of these changes. Intrapancreatic ganglia and nerve contacts in islets are maintained in diabetes. The tissue clearing and imaging approaches we have used and optimized are broadly applicable to investigating pancreatic structures and innervation in other diseases, such as pancreatitis and pancreatic cancer, and are relevant to imaging vasculature and innervation in other organs. Our data also have important translational implications. Our data suggest that the close association between islets and pancreatic nerves is maintained in human T2D; therefore, the anatomical pathways that would allow for targeted neuromodulation to regulate pancreatic function are preserved. Defining pancreatic neurocircuitry is crucial to understanding neural regulation of pancreatic function, as it elucidates anatomical pathways for direct effects on endocrine cells. Future studies will determine critical interactions between cells and nerves, whether variation in islet innervation density is associated with differences in islet function, and whether metabolic disease leads to functional deficits in islet innervation independent of structure.

Ad libitum fed C57BL/6 mice were maintained under controlled conditions (12-hour light/12-hour dark cycle, 22C). NOD mice (NOD/ShiLtJ, the Jackson Laboratory, Bar Harbor, ME, USA) and STZ-treated mice were used to model T1D. Female NOD mice aged 12 to 16 weeks with two consecutive blood glucose measurements of >300 mg/dl (morning, nonfasting) were termed diabetic. Littermates with blood glucose <200 mg/dl were used as nondiabetic controls. Multiple low-dose STZ-treated mice (males, aged 10 weeks) were generated by treating C57BL/6N mice (Charles River, Wilmington, MA, USA) intraperitoneally with freshly made STZ (40 mg/kg; Sigma-Aldrich, St. Louis, MO) in citrate-saline buffer (pH 4.5) for five consecutive days and euthanizing them at 5 or 15 days following the final STZ injection. NonSTZ-treated littermates were used as controls. All protocols were approved by the Institutional Animal Care and Use Committee.

Mice were anesthetized with isoflurane (3%) and perfused with heparinized saline followed by 4% paraformaldehyde (PFA; Electron Microscopy Sciences, Hatfield, PA, USA). Pancreata were dissected, cleared of adipose tissue, divided into duodenal and splenic regions (Fig. 1A), with the gastric lobe included with the duodenal lobe, and postfixed overnight in 4% PFA at 4C. For antibody evaluation experiments, small pancreatic samples (2 to 3 mm diameter) were assessed. On the following day, the tissue was washed in phosphate-buffered saline (PBS; 3) before proceeding with optical clearing protocols.

Human samples (Table 1) were obtained from Prodo Laboratories Inc. (Aliso Viejo, CA, USA) and postfixed in 4% PFA. Since human samples were processed upon acquisition and not simultaneously as with mouse tissue, we could not compare staining intensity between samples. All samples were harvested from the superior margin of the tail of the pancreas.

Whole-organ staining and clearing were performed using iDISCO+ (15). Dissected pancreata were dehydrated [20, 40, 60, 80, and 100% methanol at room temperature (RT)], delipidated [100% dichloromethane (DCM; Sigma-Aldrich, St. Louis, MO, USA)], and bleached in 5% H2O2 (overnight, 4C). Pancreata were rehydrated (80, 60, 40, and 20% methanol) and permeabilized [5% dimethyl sulfoxide/0.3 M glycine/0.1% Triton X-100/0.05% Tween-20/0.0002% heparin/0.02% NaN3 in PBS (PTxwH)] for 1 day. Pancreata were then placed in blocking buffer [PTxwH + 3% normal donkey serum (Jackson ImmunoResearch, West Grove, PA, USA)] at 37C overnight. Samples were incubated with primary antibodies (table S1) in blocking buffer for 3 or 6 days (small pancreatic pieces and hemipancreata, respectively) at 37C. After five washes with PTxwH at RT (final wash overnight), samples were incubated with secondary antibodies in blocking buffer (1:500) for 3 or 6 days. Samples were washed with PTxwH (five times, RT) and PBS (five times, RT), dehydrated with a methanol gradient, then washed in 100% methanol (three times, 30 min each) and DCM (three times, 30 min each), and then transferred to dibenzyl ether (DBE; Sigma-Aldrich) to clear. Primary antibody specificity was confirmed in pancreatic tissue from reporter mice expressing tdTomato in defined neural populations. There was no immunolabeling without primary antibodies using iDISCO+ or ECi. A modified iDISCO+ protocol used 0.5% Triton X-100 and 0.1% Tween-20 for the permeabilization, blocking, and primary and secondary antibody buffers.

A modified ECi tissue clearing protocol was used for samples from animals injected intravenously with fluorophore-tagged dextran (100 m, 25 mg/ml) or a direct conjugated CD31 antibody (100 m, 50 mg/ml). Tissue was harvested, postfixed, and washed with PBS as described above. Samples were incubated with 3% H2O2 (10 min, RT), washed in PBS with 0.2% Triton X-100 (Ptx2; three times over 3 h, RT), and incubated overnight in PTx2 + heparin (10 mg/ml; PTwH) and 3% normal donkey serum at RT. Samples were incubated with primary antibodies in PTwH with 3% normal donkey serum (2 days, RT) followed by PTwH washes (four times over 4 hours). Samples were incubated with secondary antibodies in PTwH with 3% normal donkey serum (2 days, RT), followed by PTwH washes as above. Optical clearing was achieved by incubating samples in 50% ethanol, 70% ethanol, 100% ethanol (all pH 9, 4 hours, 4C), 100% ethanol (pH 9, overnight, 4C), and finally one wash and one overnight incubation (RT) in ECi (Sigma-Aldrich) before imaging.

Z-stacked optical sections were acquired with an UltraMicroscope II (LaVision BioTec, Bielefeld, Germany; 1.3, 4, or 12 magnification with dynamic focus with a maximum projection filter). Human samples were imaged at 1.3 with dynamic focus and with multiple Z-stacks acquired at 4 with 20% overlap and tiled using the plugin TeraStitcher through the ImSpector Pro software (LaVision BioTec). Spatial resolutions of light-sheet images were 5 m by 5 m by 5 m at 1.3, 1.63 m by 1.63 m by 5 m at 4, and 0.602 m by 0.602 m by 2 m at 12.

Small mouse pancreatic sections were imaged in glass-bottom eight-well chambers (Ibidi, Grfelfing, Germany) filled with immersion media DBE or ECi and imaged using an inverted Zeiss LSM 880 confocal microscope with a 10 [numerical aperture (NA), 0.3] objective and a step size of 5 m. Spatial resolution for confocal images acquired at 10 was 1.67 m by 1.67 m by 5 m.

Imaris versions 9.1 to 9.3.1 (Bitplane AG, Zrich, Switzerland) were used to create digital surfaces covering the islets (1.3 and 4 images) and innervation (4 images) to automatically determine volumes and intensity data. Volume reconstructions were performed using the surface function with local contrast background subtraction. For detection of islets, the threshold factor corresponded to the largest islet diameter in each sample. For detection of nerves, the threshold factor was set to 12.2 m. A smoothing factor of 10 m was used for islets analyzed at 1.3, and a factor of 3.25 m was used for analysis of islets and nerves at 4. For detection of TH+ nerves, TH+ cells (75, 76) were manually removed from the final TH+ nerve surface by excluding volumes below 120 m3 residing within insulin+ islets and overlapping with insulin staining. The Imaris Distance Transform Matlab XTension function was used to calculate the distance of each islet surface from the innervation surface. Distances of islets are reported as the intensity minimum of the distance transformation channel (intensity 0 = islet touching nerve) for each islet surface to the nerve surface as calculated by the distance transformation operation. In confocal images, digital surfaces were created to cover nerves and individual cells, cells, or ganglia. For detection of ganglia, a region of interest was manually created around each individual ganglion to create a digital surface specifically covering cell bodies, but not nerve fibers. The Imaris Distance Transform Matlab XTension was then used as above to determine the distance between ganglia and insulin+ islets or the distance between nerves and individual or cells with a distance of 0 indicating a nerve contact. Limitations to our analyses of endocrine cell contacts include the following: We may not have captured / cells with lower staining intensity; in some cases, we could not completely separate adjacent endocrine cells and therefore counted multiple adjacent cells as a single cell; our method quantifies the number of endocrine cells contacting nerves but does not allow for quantification of number of contacts per endocrine cell.

Data are shown as means SEM. Distribution was assessed by Shapiro-Wilk test. Significance was determined by unpaired two-way t test or one-way analysis of variance (ANOVA) with post hoc Tukeys multiple comparisons test (Gaussian distribution), Mann-Whitney test, or Kruskal-Wallis test followed by Dunns multiple comparisons test (nonparametric distribution). Significance was set at an level of 0.05.

Acknowledgments: Funding: A.A. was supported by a senior postdoctoral fellowship from the Charles H. Revson Foundation (grant no. 18-25) and a postdoctoral scholarship from the Swedish Society for Medical Research (SSMF). This work was supported by the American Diabetes Association Pathway to Stop Diabetes Grant ADA #1-17-ACE-31 and, in part, by grants from the NIH (DK105015, P-30 DK020541, U01MH105941, R01NS097184, OT2OD024912, and UC4DK104211), JDRF (2-SRA-2017-514-S-B), and the Alexander and Alexandrine Sinsheimer Scholar Award. This work was supported in part by a Mindich Child Health and Development Institute Pilot and Feasibility Grant. Microscopy and image analysis were performed at the Microscopy CoRE at the Icahn School of Medicine at Mount Sinai. We wish to thank the Human Islet and Adenoviral Core (HIAC) of the NIDDK-supported Einstein-Sinai Diabetes Research Center (DRC) and the families of the donors. Author contributions: A.A., A.G.-O., A.F.S., and S.A.S. conceived and designed the study and interpreted the data. A.A. performed all light-sheet experiments and analyzed and interpreted the data. A.A., M.J.-G., and R.L. performed confocal experiments and analyzed and interpreted the data. C.R., M.J.-G., and A.A. provided STZ-treated mice. C.R. and A.G.-O. provided NOD mice and human samples. N.T. and Z.W. provided technical and methodological input. A.A. and S.A.S. drafted the manuscript with input from all other authors. All authors approved of the final submitted version of this paper. Competing interests: S.A.S. is a named inventor of the intellectual property, Compositions and Methods to Modulate Cell Activity, and is a co-founder of, consults for, and has equity in the private company Redpin Therapeutics (preclinical stage gene therapy company developing neuromodulation technologies). The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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A 3D atlas of the dynamic and regional variation of pancreatic innervation in diabetes - Science Advances

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