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Oct 25, 2022 / Oncology / Neuroscience / Immunology

Single cell publications you’ll want to read

Natalya Ortolano

Keeping up with all of the research using single cell RNA sequencing (scRNA-seq) is impossible—nearly 7,000 studies have been published already this year according to PubMed. We recently highlighted research using our Visium Spatial Gene Expression technology in 2022 and 2021 to help you parse through the ever-growing number of publications in spatial biology, so we thought we’d do the same for our OG assay, which needs no introduction (but we’ll do it anyway)—Chromium Single Cell Gene Expression.

Scientists started tinkering with protocols to sequence RNA in a single cell back in 1990 when Iscove et al. published a “simple procedure…for preparing microgram amounts of cDNA, suitable for cloning into libraries, from samples as small as a single cell” (1). In 1992, a PNAS publication analyzing the transcriptome of individual live neurons followed (2). These two landmark publications—cited 316 and 1,210 times, respectively—were the foundation for the first commercially available high-density DNA microarray chips and later adapted for the scRNA-seq method (3).

But a 2009 Nature Methods publication from researchers at the University of Cambridge was a game-changer—they were the first to perform scRNA-seq using a next-generation sequencing platform (4). Since 2009, over 75,000 papers using scRNA have been published according to PubMed.

We’re proud our products have fueled some of these findings—about 3,200 peer-reviewed publications have featured our Chromium Single Cell Gene Expression platform since its initial release in 2016, according to our publications database. Since then, researchers have revealed biology is more complex than we ever imagined, capturing the cell-to-cell variability in diseases such as cancer and schizophrenia, developing tissue, and even the immune response to SARS-CoV-2 vaccine.

Here are five articles from 2022 that exemplify the power of this increasingly popular and impactful technology.

Want to jump to topics within this blog post? Explore human brain implants in a rat, the role of gut microbes in smoking, T-cell activity in ovarian cancer, neuroinflammation in epilepsy, and the first genome-scale database of genotype–phenotype relationships.

Human brains in a rat, not a vat

Human brains are intrinsically complex, containing a labyrinth of folds not found in other species often used for disease modeling. Organoids have risen in prominence as a powerful model, but they lack the vascular and neuron-to-neuron communication that naturally occurs in living organisms.

Researchers from Stanford University bypassed this issue by implanting cortical organoids into the somatosensory cortex—a part of the brain that processes sensation (5).

The researchers used a combination of transcriptional, morphological, and functional analyses to determine how well the organoid was incorporated into the rats’ brains. They used single nuclei RNA-seq (snRNA seq) to analyze organoids differentiated for eight months in a dish and in a rat’s brain. Ex vivo slices of the organoid differentiated in the rat brain expressed higher levels of genes associated with processes observed in mature neurons, such as synaptic signaling and voltage-gated channel activity. These rat-developed human organoids also expressed genes only found in mature, actively firing neurons, including the primate-specific OSTN gene.

Much of the functional data presented in this paper reads like a science-fiction themed lucid dream. They used optogenetics to show that activation of neurons in an organoid implanted in a rat brain can affect behaviors such as thirst. And touching a rat’s whiskers activates sensory neurons in the implanted brain organoid. They even implanted a brain organoid developed using patient cell’s from individual’s with the genetic disorder Timothy’s syndrome to show that the transcriptional and morphological signatures differ from organoids derived from healthy controls.

This study introduces a bold new model for studying human neurodevelopment, igniting passion in neuroscientists everywhere.

The key to kicking a smoking habit may be in the gut

More than half of all smokers want to quit smoking and/or have tried to quit in the past year—one study reports the average tobacco user tries to quit 30 times before successfully kicking the habit (6–7). Smokers have a long list of reasons they struggle to quit, but a big one is weight gain. Smokers often gain weight when they quit (8). But researchers still don’t know exactly why.

Researchers from the Weizmann Institute of Science presented compelling evidence in a Nature study that gut microbes are partially to blame for this unwanted weight gain (9). When the researchers exposed mice to cigarette smoke, mice lost weight, but, as expected, when exposure stopped, the mice gained weight, especially mice that were fed a high-fat diet.

They analyzed plasma and fecal matter from the mice using mass spectrometry and identified several predominantly microbial-produced metabolites—mainly derivatives of the amino acid glycine including dimethylglycine (DMG) and N-acetylglycine (aceturic acid (ACG))—that correlated with weight gain during smoking cessation.

They used scRNA-seq to dig into what cellular effects these microbe-induced metabolites induce. They added DMG and ACG to the smoke-exposed mice’s high-fat diet and analyzed the transcriptome of adipose tissue immune cells, a group of cells that fuel obesity-associated inflammation. DMG supplementation led to an increase in Ly6C+ monocytes, which fuel an obesity-associated proinflammatory pathway. But mice with ACG-enriched diets had lower levels of Trem2+ macrophages, which have been previously correlated with weight gain in mice and humans. Similarly, many genes involved in obesity-related pathways, including lysosome function, were differentially expressed in mice supplemented with DMG and ACG.

The authors plan to perform more experiments to understand the different effects each of these microbial metabolites have on adipose tissue immunity and how this knowledge can help combat weight gain during smoking cessation.

Targeting T cells in ovarian cancer

The five-year-survival rate of ovarian cancer is 70% or higher if it’s diagnosed early, but if caught at a late stage, the five-year survival rate drops to 50% according to the American Cancer Society (10). There are few treatment options beyond chemotherapy for ovarian cancer—it’s even resistant to the effects of new immunotherapies. But researchers are still stumped by why ovarian cancer doesn’t respond to immunotherapies.

Researchers from the Moffitt Cancer Center combined our Chromium Single Cell Immune Profiling and Multiome ATAC + Gene Expression assays to interrogate the immune environment of ovarian cancer and identify immunotherapeutic opportunities (11).

The researchers focused on characterizing the T-cell population in patient-derived, late-stage ovarian carcinomas. Many cancer immunotherapies, particularly checkpoint inhibitors, activate T cells. In order for these to work, there must be a reservoir of T cells surrounding the tumor, ready to attack. Although people with ovarian cancer seem to have T cells ready to infiltrate the tumor, checkpoint inhibitors have failed in clinical studies.

The Moffitt Cancer Center scientists wanted to know if ovarian cancer had enough T cells to mount an immune response against the tumor with immunotherapies. Although they did find cancer-attacking T cells, they mostly belonged to a small subset of tissue-resident memory-like T cells rather than the circulating or infiltrating T cells most often activated by checkpoint inhibitors. In fact, only 3% of total T-cell receptors identified by single cell V(D)J profiling recognized tumor antigens. Patients with better disease outcomes had more of these resident T cells than others.

Overall, this study demonstrates that ovarian cancer can be targeted with immunotherapies, they just need to be fine tuned to activate the small subset of T cells with anti-cancer activity.

Neuroinflammation drives drug-refractory epilepsy

Nearly 50 million people around the world have epilepsy, according to the World Health Organization (12). Most can stave off seizures with anticonvulsants, which temper neuronal activity. However, one-third of patients have drug-refractory epilepsy (DRE) and need multiple surgeries to remove seizure-causing parts of the brain to control their condition. Scientists only have a basic understanding of how both epilepsy and anticonvulsants work, so more information about the underlying etiology of epilepsy is needed to develop new and improved drugs (13).

Many scientists and doctors have postulated that neuroinflammation is a key driver of seizures. Researchers have found pro-inflammatory cytokines produced by glial cells present in the brain hours after a seizure and brain infiltration by several types of T cells. Earlier this year, a group of researchers identified pro-inflammatory pathways activated in drug-resistant childhood epilepsy. They published the results of a multimodal single cell CITE-seq analysis of brain lesions from pediatric patients with epilepsy (14).

The scientists used scRNA-seq to analyze immune cells present in 11 brain tissue samples from 6 patients. They identified several clusters of infiltrating immune cells and microglia, the innate immune cells of the central nervous system. Microglia from patients with DRE had significantly higher expression of proinflammatory genes encoding chemokines and cytokines, including several tumor necrosis factors and interleukins.

They also used a method that combines cell sorting of physically interacting cells and scRNA-seq known as PIC-seq (15) to identify a substantial enrichment of microglia–T-cell interactions in DRE patient samples; these interactions are believed to likely amplify neuroinflammation.

These new findings, reported in Nature Neuroscience earlier this year, suggest therapies tempering inflammation may help treat DRE and explain why corticosteroids seem to decrease the frequency and intensity of seizures in people with DRE.

CRISPR-based map assigns function to every gene

A group of researchers from the MIT Whitehead Institute for Biomedical Research published the first genome-scale CRISPR-based screen with single cell RNA sequencing readouts (16)—a scaled up version of Perturb-seq first published in 2016 (17)—to determine the function of every human gene, including mitochondrial genes (14). This research is the culmination of years of highly collaborative work—a proof-of-concept paper of the technique was first published in Nature Biotechnology in 2020 (18).

The researchers performed Perturb-seq on more than 2.5 million human cells including retinal cells and blood cancer cells. The scientists mapped single cell transcriptomic changes back to CRISPR-induced genetic perturbations in both characterized and uncharacterized genes, providing new genotype–phenotype relationships and validating known effects—ultimately validating their new method.

The data is publicly available, and the authors hope others use it to inform their own research.

“It’s a big resource in the way the human genome is a big resource, in that you can go in and do discovery-based research,” Jonathan Weissman, PhD, an investigator at the Whitehead Institute and the corresponding author of the new study, told MIT News (19). “Rather than defining ahead of time what biology you're going to be looking at, you have this map of the genotype-phenotype relationships and you can go in and screen the database without having to do any experiments.”

The future of single cell

We were inspired by the research conducted using our technology in 2022, and we can’t wait to see what 2023 has in store for the single cell research community, especially as our single cell toolsets grow—notably, the new gene expression kit for fixed RNA profiling, which was highlighted in our most recent preprint.

Find more resources for Chromium Single Cell Gene Expression →

References:

  1. Brady G, et al. Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol Cell Biol 2: 17–25 (1990).
  2. Eberwine J, et al. Analysis of gene expression in single live neurons. Proc Natl Acad Sci USA 89: 3010–3014 (1992). doi: 10.1073/pnas.89.7.3010
  3. Hwang B, et al.Single-cell RNA sequencing technologies and bioinformatics pipelines.  Exper & Molec Med 50: 1–14 (2018). doi: 10.1038/s12276-018-0071-8
  4. Tang F, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6: 377–382 (2009). doi: 10.1038/nmeth.1315
  5. Revah O, et al. Maturation and circuit integration of transplanted human cortical organoids. Nature 610: 319–326 (2022). doi: 10.1038/s41586-022-05277-w
  6. Babb S, et al. Quitting Smoking Among Adults—United States, 2000–2015. Morb Mortal Wkly Rep 65:1457–64 (2017). doi: 10.15585/mmwr.mm6552a1
  7. Creamer MR, Wang TW, Babb S, et al. Tobacco Product Use and Cessation Indicators Among Adults — United States, 2018. Morb Mortal Wkly Rep 68: 1013–1019 (2019). doi: 10.15585/mmwr.mm6845a2
  8. Harris KK, et al. Metabolic effects of smoking cessation. Nat Rev Endocrinol 12: 299–308 (2016). doi: 10.1038/nrendo.2016.32
  9. Fluhr L, et al. Gut microbiota modulates weight gain in mice after discontinued smoke exposure. Nature 600: 713–719 (2021). doi: 10.1038/s41586-021-04194-8
  10. https://www.cancer.org/cancer/ovarian-cancer/detection-diagnosis-staging/survival-rates.html
  11. Anadon CM, et al. Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cells. Cancer Cell 40: 545–57 (2022). doi: 10.1016/j.ccell.2022.03.008
  12. https://www.who.int/news-room/fact-sheets/detail/epilepsy
  13. Lerche H, et al. Drug-resistant epilepsy—time to target mechanisms. Nat Rev Neurol 16: 595–596 (2020). doi: 10.1038/s41582-020-00419-y
  14. Kumar P, et al. Single-cell transcriptomics and surface epitope detection in human brain epileptic lesions identifies pro-inflammatory signaling. Nat Neurosci 25: 956–966 (2022). doi: 10.1038/s41593-022-01095-5
  15. Koch L. Transcriptional profiling of physically interacting cells. Nat Revs Gen 21: 275 (2020). doi: 10.1038/s41576-020-0229-9
  16. Replogle JM, et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185: 2559–2575 (2022). doi: 10.1016/j.cell.2022.05.013
  17. Dixit A, et al. Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 167: P1853–1866 (2016). doi: 10.1016/j.cell.2016.11.038
  18. Replogle JM, et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat Biotechnol 38: 954–961 (2020). doi: 10.1038/s41587-020-0470-y
  19. https://news.mit.edu/2022/crispr-based-map-ties-every-human-gene-to-its-function-0609