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Millions of Single-Cell Analyses Yield Most Comprehensive Human Cell Atlas Yet

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A field of playing cards showing different body tissues

There are 37 trillion or so cells in our bodies that work together to give us life. But it may surprise you that we still haven’t put a good number on how many distinct cell types there are within those trillions of cells.

That’s why in 2016, a team of researchers from around the globe launched a historic project called the Human Cell Atlas (HCA) consortium to identify and define the hundreds of presumed distinct cell types in our bodies. Knowing where each cell type resides in the body, and which genes each one turns on or off to create its own unique molecular identity, will revolutionize our studies of human biology and medicine across the board.

Since its launch, the HCA has progressed rapidly. In fact, it has already reached an important milestone with the recent publication in the journal Science of four studies that, together, comprise the first multi-tissue drafts of the human cell atlas. This draft, based on analyses of millions of cells, defines more than 500 different cell types in more than 30 human tissues. A second draft, with even finer definition, is already in the works.

Making the HCA possible are recent technological advances in RNA sequencing. RNA sequencing is a topic that’s been mentioned frequently on this blog in a range of research areas, from neuroscience to skin rashes. Researchers use it to detect and analyze all the messenger RNA (mRNA) molecules in a biological sample, in this case individual human cells from a wide range of tissues, organs, and individuals who voluntarily donated their tissues.

By quantifying these RNA messages, researchers can capture the thousands of genes that any given cell actively expresses at any one time. These precise gene expression profiles can be used to catalogue cells from throughout the body and understand the important similarities and differences among them.

In one of the published studies, funded in part by the NIH, a team co-led by Aviv Regev, a founding co-chair of the consortium at the Broad Institute of MIT and Harvard, Cambridge, MA, established a framework for multi-tissue human cell atlases [1]. (Regev is now on leave from the Broad Institute and MIT and has recently moved to Genentech Research and Early Development, South San Francisco, CA.)

Among its many advances, Regev’s team optimized single-cell RNA sequencing for use on cell nuclei isolated from frozen tissue. This technological advance paved the way for single-cell analyses of the vast numbers of samples that are stored in research collections and freezers all around the world.

Using their new pipeline, Regev and team built an atlas including more than 200,000 single-cell RNA sequence profiles from eight tissue types collected from 16 individuals. These samples were archived earlier by NIH’s Genotype-Tissue Expression (GTEx) project. The team’s data revealed unexpected differences among cell types but surprising similarities, too.

For example, they found that genetic profiles seen in muscle cells were also present in connective tissue cells in the lungs. Using novel machine learning approaches to help make sense of their data, they’ve linked the cells in their atlases with thousands of genetic diseases and traits to identify cell types and genetic profiles that may contribute to a wide range of human conditions.

By cross-referencing 6,000 genes previously implicated in causing specific genetic disorders with their single-cell genetic profiles, they identified new cell types that may play unexpected roles. For instance, they found some non-muscle cells that may play a role in muscular dystrophy, a group of conditions in which muscles progressively weaken. More research will be needed to make sense of these fascinating, but vital, discoveries.

The team also compared genes that are more active in specific cell types to genes with previously identified links to more complex conditions. Again, their data surprised them. They identified new cell types that may play a role in conditions such as heart disease and inflammatory bowel disease.

Two of the other papers, one of which was funded in part by NIH, explored the immune system, especially the similarities and differences among immune cells that reside in specific tissues, such as scavenging macrophages [2,3] This is a critical area of study. Most of our understanding of the immune system comes from immune cells that circulate in the bloodstream, not these resident macrophages and other immune cells.

These immune cell atlases, which are still first drafts, already provide an invaluable resource toward designing new treatments to bolster immune responses, such as vaccines and anti-cancer treatments. They also may have implications for understanding what goes wrong in various autoimmune conditions.

Scientists have been working for more than 150 years to characterize the trillions of cells in our bodies. Thanks to this timely effort and its advances in describing and cataloguing cell types, we now have a much better foundation for understanding these fundamental units of the human body.

But the latest data are just the tip of the iceberg, with vast flows of biological information from throughout the human body surely to be released in the years ahead. And while consortium members continue making history, their hard work to date is freely available to the scientific community to explore critical biological questions with far-reaching implications for human health and disease.


[1] Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Segrè AV, Aguet F, Rozenblatt-Rosen O, Ardlie KG, Regev A, et al. Science. 2022 May 13;376(6594):eabl4290.

[2] Cross-tissue immune cell analysis reveals tissue-specific features in humans. Domínguez Conde C, Xu C, Jarvis LB, Rainbow DB, Farber DL, Saeb-Parsy K, Jones JL,Teichmann SA, et al. Science. 2022 May 13;376(6594):eabl5197.

[3] Mapping the developing human immune system across organs. Suo C, Dann E, Goh I, Jardine L, Marioni JC, Clatworthy MR, Haniffa M, Teichmann SA, et al. Science. 2022 May 12:eabo0510.


Ribonucleic acid (RNA) (National Human Genome Research Institute/NIH)

Studying Cells (National Institute of General Medical Sciences/NIH)

Human Cell Atlas

Regev Lab (Broad Institute of MIT and Harvard, Cambridge, MA)

NIH Support: Common Fund; National Cancer Institute; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; National Institute on Drug Abuse; National Institute of Mental Health; National Institute on Aging; National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Eye Institute

Clinical Center Doctors Testing 3D-Printed Miniature Ventilator

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Small plastic device next to a thumbdrive
Caption: A USB flash drive (front) next to the 3D-printed miniature ventilator (back). Credit: William Pritchard, Clinical Center, NIH

Here at the NIH Clinical Center, we are proud to be considered a world-renowned research hospital that provides hope through pioneering clinical research to improve human health. But what you may not know is that our doctors are constantly partnering with public and private sectors to come up with innovative technologies that will help to advance health outcomes.

I’m excited to bring to you a story that is perfect example of the ingenuity of our NIH doctors working with global strategic partners to create potentially life-saving technologies. This story begins during the COVID-19 pandemic with the global shortage of ventilators to help patients breathe. Hospitals had a profound need for inexpensive, easy-to-use, rapidly mass-produced resuscitation devices that could be quickly distributed in areas of critical need.

Through strategic partnerships, our Clinical Center doctors learned about and joined an international group of engineers, physicians, respiratory therapists, and patient advocates using their engineering skills to create a ventilator that was functional, affordable, and intuitive. After several iterations and bench testing, they devised a user-friendly ventilator.

Transparent plastic mini ventilator
Caption: The miniature ventilator connected to an oxygen line (asterisk) and the breathing tube to the patient (crosshatch). The exhaust (dagger) is recessed to prevent accidental blockage. Credit: William Pritchard, Clinical Center, NIH

Then, with the assistance of 3D-printing technology, they improved the original design and did something pretty incredible: the team created the smallest single-patient ventilator seen to date. The device is just 2.4 centimeters (about 1 inch) in diameter with a length of 7.4 centimeters (about 3 inches).

A typical ventilator in a hospital obviously is much larger and has a bellows system. It fills with oxygen and then forces it into the lungs followed by the patient passively exhaling. These systems have multiple moving parts, valves, hoses, and electronic or mechanical controls to manage all aspects of the oxygen flow into the lungs.

But our miniature, 3D-printed ventilator is single use, disposable, and has no moving parts. It’s based on principles of fluidics to ventilate patients by automatically oscillating between forced inspiration and assisted expiration as airway pressure changes. It requires only a continuous supply of pressurized oxygen.

The possibilities of this 3D-printed miniature ventilator are broad. The ventilators could be easily used in emergency transport, potentially treating battlefield casualties or responding to disasters and mass casualty events like earthquakes.

While refining a concept is important, the key is converting it to actual use, which our doctors are doing admirably in their preclinical and clinical studies. NIH’s William Pritchard, Andrew Mannes, Brad Wood, John Karanian, Ivane Bakhutashvili, Matthew Starost, David Eckstein, and medical student Sheridan Reed studied and have already tested the ventilators in swine with acute lung injury, a common severe outcome in a number of respiratory threats including COVID-19.

In the study, the doctors tested three versions of the device built to correspond to mild, moderate, and severe lung injury. The respirators provided adequate support for moderate and mild lung injuries, and the doctors recall how amazing it was initially to witness a 190-pound swine ventilated by this miniature ventilator.

The doctors believe that the 3D-printed miniature ventilator is a potential “game changer” from start to finish since it is lifesaving, small, simple to use, can be easily and inexpensively printed and stored, and does not require additional maintenance. They recently published their preclinical trial results in the journal Science Translational Medicine [1].

The NIH team is preparing to initiate first-in-human trials here at the Clinical Center in the coming months. Perhaps, in the not-too-distant future, a device designed to help people breathe could fit into your pocket next to your phone and keys.


[1] In-line miniature 3D-printed pressure-cycled ventilator maintains respiratory homeostasis in swine with induced acute pulmonary injury. Pritchard WF, Karanian JW, Jung C, Bakhutashvili I, Reed SL, Starost MF, Froelke BR, Barnes TR, Stevenson D, Mendoza A, Eckstein DJ, Wood BJ, Walsh BK, Mannes AJ. Sci Transl Med. 2022 Oct 12;14(666):eabm8351.


Clinical Center (NIH)

Andrew Mannes (Clinical Center)

Bradford Wood (Clinical Center)

David Eckstein (Clinical Center)

Note: Dr. Lawrence Tabak, who performs the duties of the NIH Director, has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the 21st in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.

Gratitude for Biomedical Progress and All Those Who Make It Possible

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Group of people holding hands around a dinner table
Credit: Shutterstock/

It’s good for our health to eat right, exercise, and get plenty of rest. Still, many other things contribute to our sense of wellbeing, including making it a point to practice gratitude whenever we can. With this in mind, I can’t think of a better time than Thanksgiving to recognize just a few of the many reasons that I—and everyone who believes in the mission of the National Institutes of Health (NIH)—have to be grateful.

First, I’m thankful for the many enormously talented people with whom I’ve worked over the past year while performing the duties of the NIH Director. Particular thanks go to those on my immediate team within the Office of the Director. I could not have taken on this challenge without their dedicated support.

I’m also gratified by the continued enthusiasm and support for biomedical research from so many different corners of our society. This includes the many thousands of unsung, patient partners who put their time, effort, and, in some cases, even their lives on the line for the sake of medical progress and promising treatment advances. Without them, clinical research—including the most pivotal clinical trials—simply wouldn’t be possible.

I am most appreciative of the continuing efforts at NIH and across the broader biomedical community to further enable diversity, equity, inclusion, and accessibility within the biomedical research workforce and in all the work that NIH supports.

High on my Thanksgiving list is the widespread availability of COVID-19 bivalent booster shots. These boosters not only guard against older strains of the coronavirus, but also broaden immunity to the newer Omicron variant and its many subvariants. I’m also tremendously grateful for everyone who has—or soon will—get boosted to protect yourself, your loved ones, and your communities as the winter months fast approach.

Another big “thank you” goes out to all the researchers studying Long COVID, the complex and potentially debilitating constellation of symptoms that strikes some people after recovery from COVID-19. I look forward to more answers as this work continues and we certainly couldn’t do it without our patient partners.

I’d also like to express my appreciation for the NIH’s institute and center directors who’ve contributed to the NIH Director’s Blog to showcase NIH’s broad and diverse portfolio of promising research.

Finally, a special thanks to all of you who read this blog. As you gather with family and friends to celebrate this Thanksgiving holiday, I hope the time you spend here gives you a few more reasons to feel grateful and appreciate the importance of NIH in turning scientific discovery into better health for all.

From Brain Waves to Real-Time Text Messaging

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For people who have lost the ability to speak due to a severe disability, they want to get the words out. They just can’t physically do it. But in our digital age, there is now a fascinating way to overcome such profound physical limitations. Computers are being taught to decode brain waves as a person tries to speak and then interactively translate them onto a computer screen in real time.

The latest progress, demonstrated in the video above, establishes that it’s quite possible for computers trained with the help of current artificial intelligence (AI) methods to restore a vocabulary of more than a 1,000 words for people with the mental but not physical ability to speak. That covers more than 85 percent of most day-to-day communication in English. With further refinements, the researchers say a 9,000-word vocabulary is well within reach.

The findings published in the journal Nature Communications come from a team led by Edward Chang, University of California, San Francisco [1]. Earlier, Chang and colleagues established that this AI-enabled system could directly decode 50 full words in real time from brain waves alone in a person with paralysis trying to speak [2]. The study is known as BRAVO, short for Brain-computer interface Restoration Of Arm and Voice.

In the latest BRAVO study, the team wanted to figure out how to condense the English language into compact units for easier decoding and expand that 50-word vocabulary. They did it in the same way we all do: by focusing not on complete words, but on the 26-letter alphabet.

The study involved a 36-year-old male with severe limb and vocal paralysis. The team designed a sentence-spelling pipeline for this individual, which enabled him to silently spell out messages using code words corresponding to each of the 26 letters in his head. As he did so, a high-density array of electrodes implanted over the brain’s sensorimotor cortex, part of the cerebral cortex, recorded his brain waves.

A sophisticated system including signal processing, speech detection, word classification, and language modeling then translated those thoughts into coherent words and complete sentences on a computer screen. This so-called speech neuroprosthesis system allows those who have lost their speech to perform roughly the equivalent of text messaging.

Chang’s team put their spelling system to the test first by asking the participant to silently reproduce a sentence displayed on a screen. They then moved on to conversations, in which the participant was asked a question and could answer freely. For instance, as in the video above, when the computer asked, “How are you today?” he responded, “I am very good.” When asked about his favorite time of year, he answered, “summertime.” An attempted hand movement signaled the computer when he was done speaking.

The computer didn’t get it exactly right every time. For instance, in the initial trials with the target sentence, “good morning,” the computer got it exactly right in one case and in another came up with “good for legs.” But, overall, their tests show that their AI device could decode with a high degree of accuracy silently spoken letters to produce sentences from a 1,152-word vocabulary at a speed of about 29 characters per minute.

On average, the spelling system got it wrong 6 percent of the time. That’s really good when you consider how common it is for errors to arise with dictation software or in any text message conversation.

Of course, much more work is needed to test this approach in many more people. They don’t yet know how individual differences or specific medical conditions might affect the outcomes. They suspect that this general approach will work for anyone so long as they remain mentally capable of thinking through and attempting to speak.

They also envision future improvements as part of their BRAVO study. For instance, it may be possible to develop a system capable of more rapid decoding of many commonly used words or phrases. Such a system could then reserve the slower spelling method for other, less common words.

But, as these results clearly demonstrate, this combination of artificial intelligence and silently controlled speech neuroprostheses to restore not just speech but meaningful communication and authentic connection between individuals who’ve lost the ability to speak and their loved ones holds fantastic potential. For that, I say BRAVO.


[1] Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis. Metzger SL, Liu JR, Moses DA, Dougherty ME, Seaton MP, Littlejohn KT, Chartier J, Anumanchipalli GK, Tu-CHan A, Gangly K, Chang, EF. Nature Communications (2022) 13: 6510.

[2] Neuroprosthesis for decoding speech in a paralyzed person with anarthria. Moses DA, Metzger SL, Liu JR, Tu-Chan A, Ganguly K, Chang EF, et al. N Engl J Med. 2021 Jul 15;385(3):217-227.


Voice, Speech, and Language (National Institute on Deafness and Other Communication Disorders/NIH)

ECoG BMI for Motor and Speech Control (BRAVO) (

Chang Lab (University of California, San Francisco)

NIH Support: National Institute on Deafness and Other Communication Disorders

National Library of Medicine Helps Lead the Way in AI Research

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NIH, National Library of Medicine. The earth surrounded by a ring of data
Credit: National Library of Medicine, NIH

Did you know that the NIH’s National Library of Medicine (NLM) has been serving science and society since 1836? From its humble beginning as a small collection of books in the library of the U.S. Army Surgeon General’s office, NLM has grown not only to become the world’s largest biomedical library, but a leader in biomedical informatics and computational health data science research.

Think of NLM as a door through which you pass to connect with health data, literature, medical and scientific information, expertise, and sophisticated mathematical models or images that describe a clinical problem. This intersection of information, people, and technology allows NLM to foster discovery. NLM does so by ensuring that scientists, clinicians, librarians, patients, and the public have access to biomedical information 24 hours a day, 7 days a week.

The NLM also supports two research efforts: the Division of Extramural Programs (EP) and Intramural Research Program (IRP). Both programs are accelerating advances in biomedical informatics, data science, computational biology, and computational health. One of EP’s notable investments is focused on advancing artificial intelligence (AI) methods and reimagining how health care is delivered with the power of AI.

How to teach machines, showing for different piles of pills.
Credit: National Library of Medicine, NIH

With support from NLM, Corey Lester and his colleagues at the University of Michigan College of Pharmacy, Ann Arbor, MI, are using AI to assist in pill verification, a standard procedure in pharmacies across the land. They want to help pharmacists avoid dangerous and costly dispensing errors. To do so, Lester is using AI to develop a real-time computer vision model. It views pills inside of a medication bottle, accurately identifies them, and determines that they are the correct or incorrect contents.

The IRP develops and applies computational methods and approaches to a broad range of information problems in biology, biomedicine, and human health. The IRP also offers intramural training opportunities and supports other training aimed at pre-baccalaureate to postdoctoral students and professionals.

The NLM principal investigators use biological data to advance computer algorithms and connect relationships between any level of biological organization and health conditions. They also use computational health sciences to focus on clinical information processing and analyze clinical data, assess clinical outcomes, and set health data standards.

Four chest x-rays
Credit: National Library of Medicine, NIH

NLM investigator Sameer Antani is collaborating with researchers in other NIH institutes to explore how AI can help us understand oral cancer, echocardiography, and pediatric tuberculosis. His research also is examining how images can be mined for data to predict the causes and outcomes of conditions. Examples of Antani’s work can be found in mobile radiology vehicles, which allow professionals to take chest X-rays (right) and screen for HIV and tuberculosis using software containing algorithms developed in his lab.

For AI to have its full impact, more algorithms and approaches that harness the power of data are needed. That’s why NLM supports hundreds of other intramural and extramural scientists who are addressing challenging health and biomedical problems. The NLM-funded research is focused on how AI can help people stay healthy through early disease detection, disease management, and clinical and treatment decision-making—all leading to the ultimate goal of helping people live healthier and happier lives.

The NLM is proud to lead the way in the use of AI to accelerate discovery and transform health care. Want to learn more? Follow me on Twitter. Or, you can follow my blog, NLM Musings from the Mezzanine and receive periodic NLM research updates.

I would like to thank Valerie Florance, Acting Scientific Director of NLM IRP, and Richard Palmer, Acting Director of NLM Division of EP, for their assistance with this post.


National Library of Medicine (National Library of Medicine/NIH)

Video: Using Machine Intelligence to Prevent Medication Dispensing Errors (NLM Funding Spotlight)

Video: Sameer Antani and Artificial Intelligence (NLM)

NLM Division of Extramural Programs (NLM)

NLM Intramural Research Program (NLM)

NLM Intramural Training Opportunities (NLM)

Principal Investigators (NLM)

NLM Musings from the Mezzanine (NLM)

Note: Dr. Lawrence Tabak, who performs the duties of the NIH Director, has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the 20th in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.

How the Brain Differentiates the ‘Click,’ ‘Crack,’ or ‘Thud’ of Everyday Tasks

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A baseball player hits a ball. The word "crack" is highlighted. The word "thud" has a circle around and a diagonal line through it.
Credit: Donny Bliss, NIH; Shutterstock/Vasyl Shulga

If you’ve been staying up late to watch the World Series, you probably spent those nine innings hoping for superstars Bryce Harper or José Altuve to square up a fastball and send it sailing out of the yard. Long-time baseball fans like me can distinguish immediately the loud crack of a home-run swing from the dull thud of a weak grounder.

Our brains have such a fascinating ability to discern “right” sounds from “wrong” ones in just an instant. This applies not only in baseball, but in the things that we do throughout the day, whether it’s hitting the right note on a musical instrument or pushing the car door just enough to click it shut without slamming.

Now, an NIH-funded team of neuroscientists has discovered what happens in the brain when one hears an expected or “right” sound versus a “wrong” one after completing a task. It turns out that the mammalian brain is remarkably good at predicting both when a sound should happen and what it ideally ought to sound like. Any notable mismatch between that expectation and the feedback, and the hearing center of the brain reacts.

It may seem intuitive that humans and other animals have this auditory ability, but researchers didn’t know how neurons in the brain’s auditory cortex, where sound is processed, make these snap judgements to learn complex tasks. In the study published in the journal Current Biology, David Schneider, New York University, New York, set out to understand how this familiar experience really works.

To do it, Schneider and colleagues, including postdoctoral fellow Nicholas Audette, looked to mice. They are a lot easier to study in the lab than humans and, while their brains aren’t miniature versions of our own, our sensory systems share many fundamental similarities because we are both mammals.

Of course, mice don’t go around hitting home runs or opening and closing doors. So, the researchers’ first step was training the animals to complete a task akin to closing the car door. To do it, they trained the animals to push a lever with their paws in just the right way to receive a reward. They also played a distinctive tone each time the lever reached that perfect position.

After making thousands of attempts and hearing the associated sound, the mice knew just what to do—and what it should sound like when they did it right. Their studies showed that, when the researchers removed the sound, played the wrong sound, or played the correct sound at the wrong time, the mice took notice and adjusted their actions, just as you might do if you pushed a car door shut and the resulting click wasn’t right.

To find out how neurons in the auditory cortex responded to produce the observed behaviors, Schneider’s team also recorded brain activity. Intriguingly, they found that auditory neurons hardly responded when a mouse pushed the lever and heard the sound they’d learned to expect. It was only when something about the sound was “off” that their auditory neurons suddenly crackled with activity.

As the researchers explained, it seems from these studies that the mammalian auditory cortex responds not to the sounds themselves but to how those sounds match up to, or violate, expectations. When the researchers canceled the sound altogether, as might happen if you didn’t push a car door hard enough to produce the familiar click shut, activity within a select group of auditory neurons spiked right as they should have heard the sound.

Schneider’s team notes that the same brain areas and circuitry that predict and process self-generated sounds in everyday tasks also play a role in conditions such as schizophrenia, in which people may hear voices or other sounds that aren’t there. The team hopes their studies will help to explain what goes wrong—and perhaps how to help—in schizophrenia and other neural disorders. Perhaps they’ll also learn more about what goes through the healthy brain when anticipating the satisfying click of a closed door or the loud crack of a World Series home run.


[1] Precise movement-based predictions in the mouse auditory cortex. Audette NJ, Zhou WX, Chioma A, Schneider DM. Curr Biology. 2022 Oct 24.


How Do We Hear? (National Institute on Deafness and Other Communication Disorders/NIH)

Schizophrenia (National Institute of Mental Health/NIH)

David Schneider (New York University, New York)

NIH Support: National Institute of Mental Health; National Institute on Deafness and Other Communication Disorders

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