Longitudinal single-cell multi-omics profiling of PBMCs from 61 end-stage kidney disease (ESKD) patients with COVID-19 (580,040 cells) reveals distinct temporal immune trajectories in severe versus mild disease, emergence of a dexamethasone-associated monocyte population, and expanding T cell clones enriched for SARS-CoV-2 specificity.
0:00Welcome to Base by Base, the papercast that brings genomics to you wherever you are. Thanks for listening, and don't forget to follow and rate us in your podcast app. So imagine you were watching this huge mystery unfold.
0:11A novel virus just sweeps across the globe, right? And for some individuals, it causes, you know, a few days of mild symptoms, maybe a cough, some fatigue. Yeah, a minor inconvenience for most. Right, exactly.
0:25But then for others, it triggers this catastrophic systemic, just life-threatening crisis. And you have to ask why. What really happens when a highly vulnerable population encounters a novel pathogen? That is the $10000 question, isn't it?
0:40It really is. And I mean, for years, science has basically only been able to look at the aftermath of these biological battles. Like, well, imagine you're looking at a city's emergency response network, but only after a massive earthquake hits.
0:52You see the rubble, you see the deployed fire trucks, but you completely miss the actual decisions being made in the chaos. Yes. Exactly. But now, imagine if we suddenly gained, like, a live, high definition satellite feed of that entire emergency response right as it unfolds.
1:08Mapping the immune system day by day, single cell by single cell. That's an incredible thought. How could that level of resolution completely change our understanding of viral defense? And, you know, how could it actually rewrite the textbook on some of our most common medical treatments?
1:23Things like steroids? What's fascinating here is that we no longer have to just imagine it. We actually have the technology to literally watch the immune systems command center make those decisions in real time.
1:34Yeah, at a granular level, we've never achieved before. And the data we're getting back from that live feed, it's really challenging assumptions we've held for decades. So today we celebrate the work of Emily Stevenson, Aaron McDonald Dunlop, David C. Thomas, James E. Peters, and an extensive collaborative team from institutions, including Newcastle University, Imperial College London, and the Welcome Sanger Institute, who have advanced our understanding of the immune response and end stage kidney disease.
2:00It's such a monumental piece of research. It really is. And just for context. This was published in Cell Genomics in August 2025. Right. And to truly appreciate it, you really have to understand the specific clinical problem they set up to solve. Because they didn't just look at the general population.
2:17No, they didn't. They focus specifically on patients with end stage kidney disease or ESKD. Now, ESKD is defined by an irreversible loss of renal function. So we are talking about a glomeral or filtration rate of, well, less than 15 milliliters per minute.
2:35Which is incredibly low. It is. Without dialysis or kidney transplant, this condition is fatal. And the thing is, the kidneys aren't just biological coffee filters, you know? They are deeply integrated into the body's hormonal, cardiovascular, and hematopoleatic systems.
2:50Right, which means when the kidneys fail, the ripple effects are basically felt everywhere. Exactly. And that becomes incredibly dangerous when a novel pathogen, like SARS Kovi 2 enters the picture. Oh, absolutely.
3:01I mean, ESKD is one of the strongest risk factors for severe COVID-19. Wait, really? How strong of a risk factor are we talking? Well, a major UTA population scale study estimated that the hazard ratio for death in these patients is 3.7.
3:14Wow, 3.7. Yeah. That means an ESKD patient is nearly 4 times as likely to die from COVID-19 compared to someone without the disease. It's a massive systemic vulnerability. That's that's terrifying. And part of that vulnerability comes from this incredibly strange biological paradox happening inside the patient.
3:34this chronic systemic disease, right? So on one hand, patients with ESKD have an impaired immune system. Right. They have a really poor response to vaccines and they're highly susceptible to everyday infections.
3:46But on the other hand, they are simultaneously stuck in this chronic systemic pro-inflammatory state. That is the crucial tension here. Their immune system is somehow both exhausted and hyperactive at the exact same time.
3:59Okay, let's unpack this. It's like a car engine that is completely flooded with gas, but it has a broken spark plug. Oh I like that analogy. Right. It's highly volatile, it's overactive, yet totally ineffective at doing its actual job.
4:11So what exactly breaks down when this already chaotic system meets the SARS Kovi 2 virus? Well, to answer what breaks down? We really have to look at how the researchers tracked this response over time.
4:23A single snapshot of the blood just isn't enough to solve a mystery this complex. Because it's moving too fast. Exactly. We need a time lapse. We need to see the progression of the disease from the very moment the virus takes hold to the peak of the infection.
4:37And the methodology this team used to get that time lapse is just staggering. I mean, they used a multi-omix approach. Yeah, specifically, they utilized technology called cellular indexing of transcriptomes and epitopes by sequencing.
4:50Okay, CIT. Right. CIT set, combined with VDJ sequencing. I want to break those technologies down because they are really at the heart of this deep dive. Let's start with CIP sec. We hear the words transcriptones and epitopes, but what does that actually mean for the cells being studied?
5:07It is essentially a way to read a cell's internal diary and its external name tag at the exact same time. Okay, internal diary and external name tag. Exactly. The transcriptome refers to the RNA inside the cell.
5:21So the active genetic instructions being read right now. The epitopes are the specific surface proteins on the outside of the cell. Oh I see. Think of an epitope as a highly specific physical lock on the cell's outer wall.
5:33CIE sec uses special antibodies tagged with these tiny DNA barcodes that latch onto those locks. So the sequencing machine is reading both at once. Yes. Simultaneously, it reads the internal RNA instructions and it scans the external barcodes to see exactly what proteins the cell has pushed to its surface.
5:51That is incredible. And then they combine that cellular data with something called olink immino assays. If CIT sec is looking at the individual cells. What does the O-Link assay do? While CITC looks at the cells themselves, OLink looks at the fluid around them.
6:06You mean the blood plasma? Right, the blood plasma. It's a highly sensitive test that uses pairs of antibodies equipped with DNA strands. When these antibodies find their specific target protein floating in the blood, their DNA strands bind together.
6:20Ah, so it creates a signal. Exactly. It creates a signal that allows researchers to quantify exactly what proteins are floating freely in the bloodstream, completely independent of the cells. And the sheer scale of this combined multi-omix approach is mind blowing.
6:33They analyzed 580,040 high quality individual cells. Yeah, a massive data set. This came from 187 samples. across 61 ESTD patients. But the truly innovative step like, the part that makes this study a total gold mine is what they call the 2021 cohort.
6:53Yes, the 2021 cohort is really what makes this research so robust. During the 1st wave of the pandemic, you know, back in 2012, researchers took blood samples from ESKD patients who were acting as healthy negative controls.
7:05So they didn't have the virus at the time. Right. But then a year later, in 2021, some of those exact same patients, unfortunately caught COVID-19. Wait, so you track the exact same people before and after infection?
7:16Yes they did. I mean, that is terrible for the patients, obviously. But scientifically, that kind of inter-individual comparison is incredibly rare. It's almost unheard of at this scale. So how does looking at both RNA and surface proteins, this whole multi-omix approach, give us a better picture than just looking at their DNA before and after.
7:35Well, if we connect this to the bigger picture, your DNA is just the static blueprint. Okay. It tells you what a cell is theoretically capable of building, but it doesn't change when you get a virus. RNA and surface proteins, though.
7:49That is the active construction site. Oh, wow. That's great way to put it. Yeah, when you look at the transcript, the RNA. You are seeing exactly which instructions are being read and sent to the factory floor at that exact second.
8:01By looking at the exact same individuals before they ever got sick and then serially tracking their active construction sites during their infection. The researchers eliminated the massive background noise of human genetic diversity.
8:13That makes total sense. They could see precisely how the virus changed that specific person's immune system over time. Now that the methodology has set up this highly detailed time lapse, We can look at the unexpected plot twist the data revealed.
8:27Because this wasn't just a simple story of the immune system turning on and fighting a virus, was it? Far from it. When we examine the temporal dynamics, you know, how the immune response changes week by week, we see some fascinating shifts.
8:41Well, the researchers found that the interfere on response, which is essentially the body's early antiviral alarm system, peaks very strongly in week one. And then it gradually wanes over weeks 2 and three.
8:54Which completely makes sense, right? The alarm rings loudest when the fire 1st starts. Precisely. But when they contrasted the gene expression trajectories between patients who had a mild disease course versus those who became critically ill.
9:06They found the striking divergence. A divergence in what? In a specific type of innate immune cell called a monocyte. Wait, earlier you said RNA is the instruction manual? If a patient is severely ill with a massive inflammatory response, the monocide factory should be churning out inflammatory proteins.
9:23So the RNA instructions for those proteins should be working in overdrive, right? Is that what they saw? You would expect all the inflammatory genes to be dialed up to maximum, but that is not what they found, specifically regarding the TNF gene.
9:36Yeah, tumor necrosis factor. It produces a highly inflammatory protein called TNF alpha. High levels of TNF alpha floating in the blood are actually a hallmark of severe COVID-19. Okay, so you'd expect to see a lot of it.
9:51Right. But when they looked at the RNA inside the monocytes of these severely ill patients, the TNF gene was actually down regulated. Down regulated. It was turned down, not up. Yet, simultaneously, the olink plasma measurements showed that the TNF alpha protein in the patient's blood was highly elevated.
10:08Here's where it gets really interesting. It's like the local factory. The monocide has stopped printing the instructions to build fire alarms, but the city streets are already flooded with the actual alarms, the protein.
10:18That's a perfect analogy. And this raises an important question. Why is there a complete uncoupling of gene expression from the actual protein reality? Seriously, why? The researchers hypothesize that it is a massive negative feedback loop.
10:33The blood is already so dangerously saturated with TNF alpha protein that the monocytes are desperately pulling the emergency brake on their own internal production line to try and survive. Wow. Alternatively, that circulating protein might be getting pumped out by entirely different tissues, like the endothelial cells lining the blood vessels or macrophages deep in the lung tissue.
10:54The monosytes circulating in the blood are just reacting to an environment that is already toxic. This proves exactly why you can't just look at a single snapshot of RNA. If you only looked at the RNA, you'd think the inflammation was ending.
11:06Exactly. When in reality the blood is just completely flooded with inflammatory proteins. That completely shifts how we understand severe viral reactions. It really does. But that in coupling isn't the only surprise hidden in the data.
11:18We need to look at how medical treatments physically altered this cellular landscape, specifically with something called the Dex Monos. Oh, yes. This is perhaps one of the most profound discoveries in the entire paper.
11:31During the course of the study, it became standard clinical practice to treat severe COVID-19 patients with glucocorticoids. Right, specifically, a powerful steroid called dexamethazone. Right. And when the researchers looked at the cellular landscape of patients receiving this drug, they found a distinct, entirely new population of monocytes that simply did not exist before.
11:54Wait, hold on. They emerged only following treatment with glucocorticoids? Only following the treatment, yes. Hold on. Are we talking about a temporary state or a fundamentally new lineage of cell? Because if a standard steroid is spawning new cells, why haven't we noticed this in the decades we've been prescribing it?
12:10Well, we just didn't have the tools to see it. I mean, if you or someone you know has ever been prescribed a steroid for asthma or a bad rash, you've probably been told it just calms the immune system down.
12:21Are you saying the treatment didn't just suppress the system but actually spawned an entirely new type of cell? That is precisely what the single cell data reveals. These Dex monos were completely absent in the 2020 cohort.
12:34Which was before steroids were standard care. Exactly. before steroids were widely used for the virus. They only appeared in the 2021 cohort after the drug was administered. Historically, when we look at bulk data, we view ammunosuppressants as just that suppressors.
12:49We assume they silence inflammatory cells across the board. Right. But the ultra high resolution of CI6 shows us that dexomethazone is actively reprogramming the immune landscape. It's inducing the differentiation of a transcriptionally distinct subset of cells.
13:05That is wild. It's not just turning down the volume of the orchestra, you know? It's bringing an entirely new instrument onto the stage. We just didn't have microphones sensitive enough to hear it. until now.
13:15That is going to blow the minds of anyone who takes steroids for chronic inflammation. It completely reframes what we are doing to the body therapeutically. Absolutely. Okay, so while the innate immune system, these monocytes are frantically trying to adjust their internal factories and reacting to steroids, the adaptive immune system is mounting its own highly targeted defense, right?
13:35Yes, the adaptive immune system brings in the precision assassins, the T cells. The researchers tracked the dynamics of T cell clones to see which specific T cells were multiplying the fastest during the infection.
13:48And this is where that VDG sequencing comes in. Exactly. VDJ sequencing essentially acts like a barcode scanner for the highly unique genetic recombination happening inside amine receptors. Okay, so by scanning these receptors, Researchers could read the precise genetic code of the targeting mechanisms on the outside of the T cells.
14:06Right. And they found that the fastest expanding TCL clones carried specific sequences known to target SARSCOVI2. So the adaptive immune system is successfully identifying the threat and building an army to fight it.
14:19Even in these highly vulnerable kidney patients. It is, but what is truly remarkable is that they found what are called public T cell clones. Public clones. Yeah, this means that multiple different completely unrelated patients were generating the exact same rapidly expanding T cell clones.
14:37When they dug into the genetics, they found these shared clones formed MHC restricted motifs. All right, let's make sure we ground that concept for a second. Think of MHC, your major histocompatibility complex, as the tiny display window on the outside of your cells, where the cell posts fragments of the virus to show the T cells what to look for. Exactly.
14:59Finding shared T cell motifs across different people means that their immune systems, despite all their human genetic diversity, are converging on the exact same molecular strategy to fight the virus. They are building the exact same key to fit the exact same lock.
15:14That's that's like giving 2 different architects a pile of bricks. And they both build the exact same skyscraper without ever talking to each other. That's exactly what it's like. It shows how incredibly specific and targeted the human immune response can be.
15:27But, um, if their adaptive immune response is effectively targeting the virus, why are ESKD patients still dying at nearly 4 times the rate of the general population? That is the core of the paradox. Right.
15:40How does this ESKD response actually compare to someone with healthy kidneys who catches COVID-19? To figure this out, the team integrated their massive data set with 2 external previously published single cell data sets from COVID-19 patients who did not have kidney disease.
15:58Ah, so they could do a direct side by side comparison. Right. And the headline is the active response to the virus itself was largely similar. Really? Yeah. The timeline of the interferon response, the types of T cells multiplying it all looked very much the same between both groups.
16:12So the immune system is basically executing the exact same playbook. The difference has to be the environment the playbook is executed in. What do they find in the baseline state of these patients? When they compared the overall transcriptums, they found that ESKD patients had a massive upregulation of the TGF beta signaling pathway across all their immune cells.
16:31All of them. Yes. And here is the kicker. This upregulation was present irrespective of whether they even had COVID-19. It was their permanent baseline state. Okay. Let me make sure I am grasping the weight of this.
16:43TGF beta is a cytokine, a signaling protein that normally helps regulate inflammation and tissue repair, right? Correct. You're saying this repair signal is just permanently dialed up across their entire immune system.
16:57Yes, exactly. The genes encoding TGF beta one, two, and three were all significantly elevated across all immune cell lineages in the ESKD patients compared to healthy controls. That is wild. It is a fundamental structural alteration of their baseline immune system.
17:12So what does this all mean? If TGF beta is always high in kidney patients. Is that the flooded engine we talked about? Does this mean future therapies? Shouldn't just target a virus but this underlying baseline inflammation?
17:23If we connect this to the bigger picture, absolutely. The elevated TGF beta pathway appears to be the underlying signature of ESKD's immune dysfunction, TGF beta is highly involved in tissue fibrosis scarring, which is a hallmark of failing kidneys.
17:39By having their entire immune system constantly bathed in the signal, their cells are fundamentally primed differently. So when a novel virus hits. When it hits, the system just doesn't have the normal dynamic range to respond safely.
17:53The engine is already flooded. Wow. To protect these vulnerable patients in the future, we likely need to address this baseline TGF beta overactivity, not just the acute viral infection. That is a massive paradigm shift for how we treat chronic kidney disease in the context of infectious disease.
18:09But then, you know, the Dex monos discovery really highlights the power of looking at live multi-omix data rather than just relying on surface level clinical observations. It really does. But even with data this deep.
18:20How did the researchers avoid being tricked by false correlations? Because, I mean, when you have 580,000 cells with the data, you are bound 29 things that look important, but are actually just noise. Like, I'm looking at their data on a gene called Pill A C 8, for instance.
18:35Oh, this is a phenomenal example of rigorous science in the face of massive data sets. In their initial analysis, they noticed that the Pile C8 gene was significantly upregulated in the monosides of patients with severe disease.
18:49Now previous and vitro studies, you know, studies in a dish, suggested that pill AC 8 might make lung cells more permissive to SARS COVID 2 infection. Right. So it would be very, very easy to jump to the conclusion that high Pelley C 8 expression in these patients is actually causing their severe disease course.
19:06Right. The classic correlation equals causation trap. You see a fire, you see firefighters, so you assume the firefighters started the fire. Exactly. So how did they approve Pelace 8 was just a bystander?
19:15They used a really powerful statistical technique called Mendelian randomization. Because we all inherit random genetic variations at birth. Some people are naturally genetically predisposed to produce more PLC 8 than others.
19:29Okay, so it's like nature's own randomized clinical trial. Yes. The researchers looked at large genetic databases to find people with the genetic variants that naturally cause high PLAC8 expression. And if PLC 8 truly cause severe COVID-19, then the people genetically predisposed to have high PLAC 8 should have had statistically worse outcomes when they caught the virus, right?
19:49Exactly, but they didn't. I see. The Mendelian randomization proved that genetic variants influencing PLAC 8 expression had no significant association with COVID-19 severity. Fascinating. It proved beyond a doubt that PLAC 8 was not a causal driver.
20:03It was just a bystander reacting to the severe inflammation. It shows exactly why observational data needs careful multi-layered causal analysis before we start designing drugs to target specific genes.
20:15It is such a brilliant way to use the natural genetic lottery to verify an observation. But it also speaks to the author's transparency, you know, because they are very clear about the limitations of their own observational data.
20:31Yes, transparency is key here. This is a single center study, meaning the samples came from one specific geographic area and hospital system, which could obviously introduce some bias. And crucially, they are relying entirely on peripheral blood, blood drawn from a vein in the arm.
20:47Which is a very important distinction to keep in mind. The blood is the highway of the immune system, but the actual battle is happening deep inside the tissues, like the lungs or the endothelial lining of the blood vessels.
21:00The cells circulating in the arm might not perfectly match the localized inflammation happening deep inside the lung tissue. You are seeing the troops moving to and from the front lines, but you aren't standing on the battlefield itself.
21:12We basically have to extrapolate the tissue damage based on the convoys moving down the highway. That's a great way to think about it. Right. But even from the highway, the view they've given us is extraordinary.
21:22Let's pull all of this together. This deep dive reveals that while ESKD patients mount a similar review response to COVID-19 as the general public, their baseline immune system is fundamentally altered by elevated TGF beta signaling.
21:35Yes. Furthermore, longitudinal multi-omix uncovers hidden dynamics, like the uncoupling of gene expression from actual protein levels and the emergence of entirely distinct immune cell populations triggered by standard steroid treatments.
21:49It is a perfect synthesis. We are officially moving past the era of static biology into high definition temporal mapping. So what does this mean for how we treat other autoimmune and inflammatory diseases with steroids now that we know these drugs might be creating entirely new immune cell states rather than just silencing the old ones?
22:08It's a question that could redefine immunology and pharmacology for the next decade. This episode was based on an open access article under the CCBY 4.0 license. You can find a direct link to the paper and the license in our episode description.
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