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bioRxiv2022

Accurate genotype-based demultiplexing of single cell RNA sequencing samples from non-human animals

Single cell sequencing technologies (scRNA-seq, scATAC-seq, etc.) have revolutionized the study of complex tissues and unique organisms, providing researchers with a much needed species agnostic tool to study biological processes at the cellular level. To date, scRNA-seq technologies are expensive, require sufficient cell quantities, and need biological replicates to avoid batch effects or artifactual results. Pooling cells from multiple individuals into a single scRNA-seq library can address these problems. However, sample labeling protocols for facilitating the computational separation of pooled scRNA-seq samples, termed demultiplexing, have undesirable limitations, particularly in resource-limited organisms. One promising solution developed for use in humans exploits the genetic diversity between individuals (i.e., single nucleotide polymorphisms (SNP)) to demultiplex pooled scRNA-seq samples. The use of SNP-based demultiplexing methods has not been validated for use in non-human species, but the widespread use of SNP-based demuxers would greatly facilitate research in commonly used, emerging, and more obscure species. In this study we applied SNP-based demultiplexing algorithms to pooled scRNA-seq datasets from numerous species and applied diverse ground truth confirmation assays to validate genetic demultiplexing results. SNP-based demultiplexers were found to accurately demultiplex pooled scRNA-seq data from species including zebrafish, African green monkey, Xenopus laevis, axolotl, Pleurodeles waltl, and Notophthalmus viridescens. Our results demonstrate that SNP-based demultiplexing of unlabeled, pooled scRNA-seq samples can be used with confidence in all of the species studied in this work. Further, we show that the only genomic resource required for this approach is the single-cell sequencing data and a de novo transcriptome. The incorporation of pooling and SNP-demultiplexing into scRNA-seq study designs will greatly increase the reproducibility and experimental options for studying species previously limited by technical uncertainties, computational hurdles, or limited cell quantities.

bioinformatics↗

bioRxiv2025

Determining gene specificity from multivariate single-cell RNA sequencing data

An important application of single-cell genomics experiments is to identify genes specific to biological categories or experimental conditions. Although numerous approaches have been proposed to identify such genes, we consider an axiomatic approach based on defining properties that a specificity measure should have. This leads us to develop ember (Entropy Metrics for Biological ExploRation), which we show is the only method satisfying four key desired properties for a specificity measure. Applying ember to eight tissues from eight founder mouse strains, we find that gene specificity is often unintuitive: canonical markers can be supplanted, housekeeping genes are context-dependent, and mouse strain can drive unexpected cell type switching. Unsupervised learning on entropy metrics uncovers shared genes specialized to male gonads and kidney, as well as genes specific to non-consecutive developmental stages in the kidney. To facilitate further exploration of gene specificity in mice, we have also developed a comprehensive specificity database, along with a web interface, API and MCP server. Extending ember to a human PBMC dataset collected from 255 diverse individuals, we find that variation in PBMCs is largely localized to classical monocytes. We also find genes with unique specificity by sex, age and ancestral background. Together, these applications establish ember as a powerful tool and provide a roadmap for elucidating the impact of human genetic variation using the murine model.

bioinformatics↗

bioRxiv2021

Isolating and Cryo-Preserving Pig Skin Cells for Single Cell RNA Sequencing Study

The Pigskin architecture and physiology are similar to these of humans. Thus, the pig model is valuable for studying skin biology and testing therapeutics for skin diseases. The single-cell RNA sequencing technology allows quantitatively analyzing cell types, cell states, signaling, and receptor-ligand interactome at single-cell resolution and at high throughput. scRNA-Seq has been used to study mouse and human skins. However, studying pigskin with scRNA-Seq is still rare. Here we described a robust method for isolating and cryo-preserving pig single cells for scRNA-Seq. We showed that pigskin could be efficiently dissociated into single cells with high cell viability using the Miltenyi Human Whole Skin Dissociation kit and the Miltenyi gentleMACS Dissociator. Also, we showed that the subsequent single cells could be cryopreserved using DMSO without causing additional cell death, cell aggregation, or changes in gene expression profiles. Using the developed protocol, we were able to identify all the major skin cell types. The protocol and results from this study will be very valuable for the skin research scientific community.

cell biology↗

bioRxiv2020

SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

A main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called "dropout"), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

bioinformatics↗

bioRxiv2026

Accurate and scalable demultiplexing of single-cell RNA sequencing using BEACON

Barcode-based multiplexing strategies can significantly increase sample throughput and decrease costs, while mitigating batch effects of single-cell RNA sequencing experiments. However, these approaches can be limited by inaccurate or inefficient demultiplexing, resulting in cell loss and reduced statistical power. Here, we present BEACON, a novel sample demultiplexing method that efficiently learns the background count distribution from data and removes it from individual cells, thereby improving classification accuracy. BEACON outperforms other state-of-the-art methods on multiple human data sets. We apply it to cancer cell line time course experiments in vitro, enabling the identification of genes associated with aggressive tumors in vivo. Finally, we adapt BEACON to multimodal protein-transcriptome profiling, enhancing protein signal recovery to identify a CD161-positive effector memory CD4 T-cell population with a Th17-like phenotype, which we prospectively validate. BEACON can therefore be applied to other droplet-based single-cell sequencing methodologies.

bioinformatics↗

bioRxiv2019

Optimal design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power.

bioinformatics↗

bioRxiv2019

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities.

systems biology↗

bioRxiv2023

A brand-new clustering method and analysis system designed for revealing the truth of the high-dimension large data deciphered the complex composition structure of human brain endothelial cells from single- cell RNA sequence data

The clustering method is the key to high dimensional large data analysis, especially for single-cell NGS data in biological science and biomedicine sectors. Those data require a hierarchical clustering method to unveil important biological features including differentiation patterns, stem cell identifications, cell sub-type discovery, and so on. Traditional hierarchical clustering has several issues to be applied to large high-dimension data. There are a few new approaches invented recently trying to fill in the blank. However, these approaches were either based on low-dimension or down-sampled data after dimension reduction (Anibal et al., 2022) from methods like PCA or consumed an enormous amount of computing resources to get a massive number of layer levels with highly limited interpretable information. In order to create a practically available solution, I invented an entirely new hierarchical clustering method called the BW method which can be directly applied to high-dimension large data without a requirement for dimension reduction or massive computing resources. I applied BW clustering to six single-cell RNA sequence sample data. BW clustering brought deep insight into these sample data including sub-type, differentiation branch, cell state changes (development, aging process), and gene expression instability. BW-generated layers were very concise. For almost nineteen thousand cells, BW clustering only yielded 9 layers. An analysis system was created based on the BW clustering method which can unprecedentedly display the true form of high dimensional data space. The resource BW required is also very low as all the work done in this paper used a 16GB memory laptop only, making it easily accessible to researchers with limited computing resources. Overall, the BW clustering method represents a major advancement in high-dimensional large data analysis for biological and biomedical applications.

bioinformatics↗

bioRxiv2021

Re-investigation of classic T cell subsets and identification of novel cell subpopulations by single-cell RNA sequencing

Classic T cell subsets are defined by a small set of cell surface markers, while single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets remain unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naive and CD8 Naive were mainly clustered into their scCPop counterparts, while cells from the other T cell subsets were assigned to multiple scCPops including mucosal-associated invariant T cells and natural killer T cells. The multiple T cell subsets that form a single scCPop exhibited similar expression pattern, but not vice versa, indicating scCPops are much homogeneous cell populations with similar cell states. Interestingly, we discovered and named IFNhi T, a new T cell subpopulation that highly expressed Interferon Signaling Associated Genes (ISAGs). We further enriched IFNhi T by FACS sorting of BST2 for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE plot after removing ISAGs, while IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ T cells and BST2- T cells showing different efficiencies of T cell activation indicates high level of ISAGs may contribute to quick immune responses.

genomics↗

bioRxiv2026

Dynamic Schlafen 11 (SLFN11) expression revealed by single-cell RNA sequencing of clonal human cancer cells

Schlafen 11 (SLFN11) is a key determinant of sensitivity to DNA-damaging therapies in multiple cancers including leukemia, Ewing sarcoma, head and neck cancers, mesothelioma and small cell lung cancer (SCLC). Here, utilizing immunofluorescence microscopy and single-cell RNA sequencing we characterized SLFN11 expression across eight different human cancer cell lines and found cell-to-cell heterogenous expression both at the protein and RNA levels. Gene set enrichment and pathway analyses show that SLFN11 is coexpressed with a distinct stress-response state and enhanced genome maintenance programs while being inversely correlated with protein translation pathways. Publicly available single-cell RNA-sequencing datasets of human SCLC tumors confirmed the heterogeneous distribution of SLFN11 expression and confirmed its association with replication-related transcriptional programs. These findings indicate that heterogeneous SLFN11 expression reflects dynamic regulation at the single-cell level. They also underscore the limitations of bulk proteomic and transcriptome profiling and point to a potential reservoir of intrinsic chemotherapy resistance.

cancer biology↗

bioRxiv2019

Single-cell RNA sequencing reveals mRNA splice isoform switching during kidney development

During mammalian kidney development, nephron progenitors undergo a mesenchymal to epithelial transition and eventually differentiate into the various tubular segments of the nephron. Recently, the different cell types in the developing kidney were characterized using the Dropseq single cell RNA sequencing technology for measuring gene expression from thousands of individual cells. However, many genes can also be alternatively spliced and this creates an additional layer of heterogeneity. We therefore used full transcript length single-cell RNA sequencing to obtain the transcriptomes of 544 individual cells from mouse embryonic kidneys. We first used gene expression levels to identify each cell type. Then, we comprehensively characterized the splice isoform switching that occurs during the transition between mesenchymal and epithelial cellular states and identified several putative splicing regulators, including the genes Esrp1/2 and Rbfox1/2. We anticipate that these results will improve our understanding of the molecular mechanisms involved in kidney development.

genomics↗

bioRxiv2019

Single Cell RNA Sequencing of Calvarial and Long Bone Endocortical Cells

Single cell RNA-seq (scRNA-seq) is emerging as a powerful technology to examine transcriptomes of individual cells. We determined whether scRNA-seq could be used to detect the effect of environmental and pharmacologic perturbations on osteoblasts. We began with a commonly used in vitro system in which freshly isolated neonatal mouse calvarial cells are expanded and induced to produce a mineralized matrix. We used scRNA-seq to compare the relative cell type abundances and the transcriptomes of freshly isolated cells to those that had been cultured for 12 days in vitro. We observed that the percentage of macrophage-like cells increased from 6% in freshly isolated calvarial cells to 34% in cultured cells. We also found that Bglap transcripts were abundant in freshly isolated osteoblasts but nearly undetectable in the cultured calvarial cells. Thus, scRNA-seq revealed significant differences between heterogeneity of cells in vivo and in vitro. We next performed scRNA-seq on freshly recovered long bone endocortical cells from mice that received either vehicle or Sclerostin-neutralizing antibody for 1 week. Bone anabolism-associated transcripts were also not significantly increased in immature and mature osteoblasts recovered from Sclerostin-neutralizing antibody treated mice; this is likely a consequence of being underpowered to detect modest changes in gene expression, since only 7% of the sequenced endocortical cells were osteoblasts, and a limited portion of their transcriptomes were sampled. We conclude that scRNA-seq can detect changes in cell abundance, identity, and gene expression in skeletally derived cells. In order to detect modest changes in osteoblast gene expression at the single cell level in the appendicular skeleton, larger numbers of osteoblasts from endocortical bone are required.

cell biology↗

bioRxiv2025

Single-cell RNA sequencing of murine liver reveals an aligned circadian clock and cell-population specific circadian regulated pathways

The circadian clock is tightly connected to metabolism, which is evident in various metabolic processes performed by the liver. Perturbation of these processes due to circadian dysregulation leads to liver specific pathology. The liver is composed of multiple different cell populations each with distinct functions contributing to organ homeostasis, but individual cell population contributions to circadian clock function is not yet known. Single-cell RNA sequencing provides the opportunity to understand clock function and oscillating gene expression within an organ system at the individual cell population level that would allow for better understanding of the crosstalk between the circadian clock and metabolic pathways within the liver. In the past, barriers to achieving this goal included complexity associated with generating single-cell RNA sequencing time series data as well as the complexity of data analysis. Here, we established a protocol that enabled the generation of murine liver cell population time series data, as well as a methodological approach to evaluate the core molecular clock and oscillating gene expression in individual cell populations. Using a combination of normalized coefficient of variation, clock-correlation and aggregate pseudobulk, we found a robust and aligned circadian clock in each of the cell populations. We then employed a pseudoreplicate / pseudobulk strategy to identify oscillating gene expression and benchmarked against bulk RNA sequencing data; we demonstrated that many metabolic genes were oscillating in several of the cell populations, including non-hepatocyte clusters. Finally, we identified oscillating genes unique to specific cell populations that play critical roles in liver function. The findings in this study lay an important foundation for understanding clock function and contributions of oscillating gene function at the individual cell population level in liver.

molecular biology↗

bioRxiv2024

Single cell RNA sequencing reveals a shift in cell function and maturation of endogenous and infiltrating cell types in response to acute intervertebral disc injury

Intervertebral disc (IVD) degeneration contributes to disabling back pain. Degeneration can be initiated by injury, and progressively leads to irreversible cell loss and loss of IVD function. Attempts to restore IVD function through cell replacement therapies have had limited success due to knowledge gaps in the critical cell populations and molecular crosstalk after injury. Here, we used single cell RNA sequencing to identify the transcriptional changes of endogenous cells of the IVD and infiltrating cell populations following IVD injury. Control and Injured coccygeal IVDs were extracted from 12 week old female C57BL/6J mice 7 days post injury and subjected to single-cell resolution transcriptomic sequencing. Clustering, gene ontology, and pseudotime trajectory analyses determined transcriptomic divergences in the cells of the Injured IVD, flow cytometry identified they types of infiltrating immune cells, and immunofluorescence was utilized to define mesenchymal stem cell (MSC) localization. Clustering analysis revealed 11 distinct cell populations that included IVD, immune, vascular cells, and MSCs. Differential gene expression analysis determined that Outer Annulus Fibrosus, Neutrophils, Saa2-High MSCs, Macrophages, and Krt18+ Nucleus Pulposus (NP) cells were the major drivers of transcriptomic differences between Control and Injured cells. Gene ontology revealed that the most upregulated biological pathways were angiogenesis and T cell-related while wound healing and ECM regulation categories were downregulated. Pseudotime trajectory analyses revealed that IVD injury directed cells towards increased differentiation in all clusters, except for Krt18+ NP cells which remained in a less mature cell state. Saa2-High and Grem1-High MSCs populations drifted towards more differentiated IVD cells profiles with injury and localized distinctly within the IVD. This study revealed novel MSC populations in a heterogeneous landscape of IVD cell populations during injury, and these cells may be leveraged for future IVD repair studies. Lay SummaryThe intervertebral disc (IVD) is a spinal joint that accumulates damage with age but has limited tissue repair capabilities. IVD injuries progress into degeneration, and IVD degeneration is a leading cause of lower back pain. Understanding the cell populations that change and respond to injury will uncover targets to restore IVD function. Mesenchymal stem cells (MSCs) are cells within the IVD that can potentially replenish the cells lost after IVD damage. To better understand how IVD cell populations are affected by tissue damage, we performed single cell RNA sequencing of IVD tissue 7 days post injury and analyzed the differences in gene regulation. We identified diverse cells populations such as IVD, immune cells, vascular cells, and MSCs. We discovered the presence of Saa2 and Grem1 expressing MSCs that become less stem cell-like and express higher levels of IVD gene markers after injury. We also determined that Saa2 and Grem1 have varying gene expression patterns in IVD tissues that becomes attenuated after injury. These MSCs could be targeted for future stem cell therapies to prevent IVD degeneration.

cell biology↗

bioRxiv2024

CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data

With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviours of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the post-perturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting latent features for both controlled and perturbed cells via two encoders, subsequently engaging in mutual translation within the latent space through two nonlinear mappings via a coupler, and ultimately generating controlled and perturbed data by two separate decoders to process the encoded and translated features. CoupleVAE facilitates a more intricate state transformation of single cells within the latent space. Experiments in three real datasets on infection, stimulation and cross-species prediction show that CoupleVAE surpasses the existing comparative models in effectively predicting single-cell RNA-seq data for perturbed cells, achieving superior accuracy.

bioinformatics↗

bioRxiv2019

SciBet: An ultra-fast classifier for cell type identification using single cell RNA sequencing data

Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell type identification.

bioinformatics↗

bioRxiv2026

Limitations of differential expression for cell-type marker discovery in single-cell RNA sequencing atlases

Cell-type marker genes are definitional characteristics of a cell type, and in single-cell RNA sequencing atlases they are commonly nominated through differential expression (DE) testing. The Wilcoxon rank-sum test is the default method for marker gene discovery in Seurat and a recommended method in Scanpy, two widely used computational software packages for scRNA-seq analysis. Structurally, Wilcoxon's standardized statistic grows with sample size at a fixed underlying effect, meaning computed rankings across clusters of unequal size are incomparable. We quantified this effect across three atlas datasets - lung, kidney, and brain. Mean Wilcoxon score scaled with log cluster size at Pearson correlation r = 0.90-0.95 in each organ. In contrast, a classification-based comparator, NS-Forest, showed low correlation with cluster size (r = 0.10-0.28). This DE-cluster size relationship remained essentially unchanged after controlling for classification performance through partial correlation, decayed monotonically under cluster size downsampling, and was reproduced in simulation with marker quality fixed. The Wilcoxon rank-sum test can be transformed into a rank-based effect size by normalizing by the target-background pair count, removing the positive size dependence. However, this effect size nominates an identical set of candidate genes as the original statistic, indicating the cluster size dependence and poor specificity of DE nominated genes are separate problems. Genes nominated under default settings were substantially less specific to their target clusters, with median on-target fractions of 0.204 (lung), 0.166 (kidney), and 0.083 (brain), compared to NS-Forest's marker genes (0.580, 0.687, 0.364), revealing the relatively poor specificity that highly scoring DE genes exhibit.

bioinformatics↗

bioRxiv2025

Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma

BackgroundAsthma is driven by complex interactions amongst structural airway cells, cells of the immune system, and the environmental. While sputum cell characterization has been instrumental in studying asthma pathogenesis and refining treatment strategies, the nuances of cellular transcriptomes and intercellular communication in asthmatic sputum remain poorly understood. MethodsWe employed single-cell RNA sequencing to analyze cells isolated form the sputum from 16 asthma patients and 8 non-asthmatic controls. Cell identities were established using curated marker genes and SingleR annotation. We compared cell-specific gene expression and communication networks between asthmatic and control groups, correlating findings with distinct pathways that were dysregulated in asthma. Findings37,565 cellular transcriptomes were captured and analyzed. 15 distinct cell populations were identified, including various macrophages, monocytes, dendritic cells, and lymphocytes, along with rare cell types such as mast cells, innate lymphoid cells, bronchial epithelial cells, and eosinophils. Intercellular communication analysis indicated heightened signaling activity in asthma compared to controls, particularly in CD4+ T cells and dendritic cells which exhibited the most significant increases in RNA expression of outgoing signaling molecules. Notably, the ADAM12-SDC4 and CCL22-CCR4 ligand-receptor pathways demonstrated the strongest shifts between asthma and control subjects, particularly between dendritic cells and CD4 lymphocytes. InterpretationSC RNA seq profiling the asthma cellular transcriptome analysis of sputum highlights both innate and adaptive immune mechanisms that are significantly amplified in asthma. The elevated expression of ADAM12-SCD4 and CCL22-CC4 point to their critical role in asthma pathogenesis, suggesting potential avenues for targeted therapies and improved management of this chronic condition. Research in contextO_ST_ABSEvidence Before This StudyC_ST_ABSAsthma is a chronic inflammatory disease of the airways driven by intricate interactions between airway structural and immune cells. Previous transcriptomic studies have focused on bulk RNA samples from the airway, leaving significant gaps in our understanding of the cellular dynamics that characterize the disease. Added Value of This StudyThis study pioneers the use of single-cell RNA sequencing on sputum samples from patients with asthma, revealing a detailed landscape of cell phenotypes and dynamic communication patterns that distinguish asthmatic individuals from those without the disease. Notably, heightened intercellular communication was observed in asthma, particularly between CD4+ T cells and dendritic cells, confirming that there is a robust network of interactions between immune and structural cells. The notable increase of ADAM12-CCR4 communication from dendritic cells to other cell populations further emphasizes the dysregulation present in asthma. Implications of All Available EvidenceOur transcriptomic profiling illuminates distinct and amplified communication pathways involving CD4+ T cells and dendritic cells, aligning with established paradigms of both adaptive and innate immune responses in asthma pathogenesis. The identification of ADAM12 and CCR4 pathway dysregulation adds a critical layer to our understanding of the molecular mechanisms underpinning asthma, paving the way for potential therapeutic targets and personalized treatment strategies. Single cell profiling of the sputum has the capacity to characterize the breadth of cellular phenotypes, their functional status, and the communication in the airway at a level not previously attainable.

genomics↗