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Results for “single cell RNA sequencing”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Deciphering Tumor Microenvironment Dynamics in Tumorigenesis and Lymph Node Metastasis of Esophageal Squamous Cell Carcinoma using Single-Cell RNA Sequencing

Esophageal squamous cell carcinoma (ESCC) exhibits profound inter-tissue heterogeneity, yet how immune and stromal ecosystems diverge between primary tumors and metastatic lymph nodes remains poorly understood. Here, we generated a single-cell atlas of 344,790 cells and paired T cell receptor profiles from primary tumor mucosa, adjacent mucosa, non-metastatic lymph nodes, and metastatic lymph nodes from 18 patients with treatment-naive ESCC. We identify compartment-specific immune and stromal architectures that shape distinct antitumor responses and suppressive programs. Primary tumors were characterized by marked enrichment of regulatory T cells (TREG) and activated dendritic cells coordinated through CTLA4-associated circuits, accompanied by accelerated CD8+ T cell differentiation toward intermediate and terminal exhaustion with loss of cytotoxicity. In contrast, metastatic lymph nodes preserved substantial pools of pre-exhausted CD8+ T cells with retained effector potential and strong clonal connectivity to the primary tumor, suggesting sustained antigen-driven trafficking. However, these reinvigoration-competent populations were embedded within a niche dominated by TREM2high macrophages that delivered SPP1-dependent suppressive signals to exhausted and regulatory T cells. Stromal lineages also displayed niche-specific specialization: tumor mucosa contained chemokine-rich inflammatory fibroblasts, whereas metastatic lymph nodes upregulated extracellular matrix remodeling programs. Together, our findings demonstrate that ESCC progression is governed by anatomically distinct exhaustion trajectories, clonal behaviors, stromal states, and suppressive circuits. This framework provides mechanistic insight into why lymph node response is a key determinant of clinical outcome and highlights the need for site-tailored immunomodulation strategies that target TREG-mediated suppression in primary tumors and TREM2high macrophage programs in metastatic nodes.

cancer biology↗

bioRxiv2026

Pan-cancer analysis of single-cell RNA sequencing data from 304 human tumors sheds light on the aneuploidy paradox

Aneuploidy poses a central paradox in cancer biology: it impairs cellular fitness in normal cells but drives cancer progression. To resolve this, we analyzed single cell transcriptomes from >665,000 cells - including [~]288,000 malignant cells - across 304 tumors and 15 cancer types. Integrating transcriptomics with inferred aneuploidy profiles, we characterized cell-intrinsic programs and interactions with the tumor microenvironment. Unexpectedly, highly aneuploid single cells exhibited reduced proliferation and metabolism, contrasting sharply with tumor-bulk profiles. We show this divergence is driven by karyotypic heterogeneity: in highly heterogeneous tumors, aneuploid cells display signatures of acute stress and negative selection. Conversely, in clonally aneuploid tumors, these detrimental signatures are lost and replaced by signatures of increased proliferation and enhanced metabolism, reflecting adaptation. Additionally, we identified consistent transcriptional programs driven by recurrent chromosome-arm alterations across both single cells and bulk tumors. These findings illuminate the selective forces shaping tumor evolution and the aneuploidy paradox. Statement Of SignificanceBy jointly evaluating gene expression and aneuploidy at single-cell resolution, we demonstrate that karyotypic heterogeneity underlies the transcriptional impact of aneuploidy, reveal distinct cellular responses to emerging versus stable states, and identify recurrent chromosomal alterations that drive conserved transcriptional programs. Our findings capture the dynamics of aneuploidy evolution in human tumors, providing novel insights into the aneuploidy paradox.

cancer biology↗

bioRxiv2024

Temporally resolved single-cell RNA sequencing reveals protective and pathological responses during herpes simplex virus 1 CNS infection

Herpes Simplex Virus 1 (HSV-1) is a common human neurotropic virus with the majority of adults harboring latent-recurrent infections. In rare cases, HSV-1 infection can access the central nervous system through the neuronal route and develop into life-threatening encephalitis. Here, we used a mouse model for HSV-1 infection to describe the transcriptomic profile of the infected brain stem at the single-cell level and with temporal resolution. Among resident brain cells, microglia increased in proportion during the course of infection, while astrocytes, pericytes, and endothelial cell levels decrease. At the levels of peripheral immune cells, we found notably monocytes to strongly influx the infected brain. Large dynamic changes were found in the abundance of subpopulations of the different cell types following virus infection. For instance, we identify one subpopulation of microglia exhibiting very high type I interferon and chemokine expression early during infection. This population was also enriched for viral transcripts, suggesting localization at foci of infection, and orchestrating recruitment of other immune cells. In contrast, for the infiltrating monocytes, we identified a larger panel of unique subpopulations with antiviral and inflammatory phenotypes, and found not all of these being highly positive for viral transcripts, thus indicating monocyte activities beyond the infected brain areas. Finally, investigation of endothelial cell cross-talk with other cell types revealed that cytokines derived from microglia and monocyte, but also T cells, contribute to disturbance of the blood brain barrier. Our work thus reveals for the first time the complex nature of the cellular response in the virus-infected brain, which seeks to eliminate infection but can also prime for pathological changes.

immunology↗

bioRxiv2022

Consensus Label Propagation with Graph Convolutional Networks for Single-Cell RNA Sequencing Cell Type Annotation

MotivationSingle-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. ResultsWe present a Graph Convolutional Network (GCN) based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq data sets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a non-parametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. AvailabilityOur code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP Contactd_lewinsohn@coloradocollege.edu Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗

bioRxiv2024

Cocaine-Induced Gene Regulation in D1 and D2 Neuronal Ensembles of the Nucleus Accumbens Revealed by Single-Cell RNA Sequencing

Cocaine use disorder is characterized by persistent drug-seeking behavior and a high risk of relapse, driven by lasting molecular and circuit adaptations in the nucleus accumbens. To explore the transcriptomic changes underlying these alterations, we employed fluorescence-activated nucleus sorting coupled with single-nucleus RNA sequencing to analyze D1 and D2 medium spiny neurons in this brain region of male mice subjected to acute cocaine exposure or to prolonged withdrawal from repeated cocaine exposure without or with an acute cocaine rechallenge. This approach allowed us to precisely delineate and contrast transcriptionally distinct neuronal subpopulations[boxh]or ensembles - across various treatment conditions. We identified significant heterogeneity within both D1 and D2 MSNs, revealing distinct clusters with unique transcriptional profiles. Notably, we identified a discrete D1 MSN population characterized by the upregulation of immediate early genes, as well as another group of D1 MSNs linked to prolonged withdrawal, uncovering novel regulators of withdrawal-related transcriptome dynamics. Our findings provide a high-resolution transcriptomic map of D1 and D2 MSNs, illustrating the dynamic changes induced by cocaine exposure and withdrawal. These insights into the molecular mechanisms underlying cocaine use disorder highlight potential targets for therapeutic intervention aimed at preventing relapse.

neuroscience↗

bioRxiv2022

Single-cell RNA sequencing of conditional Pten knockout neurons in mice

PTEN is a well-known tumor suppressor whose mutations are also strongly associated with Autism Spectrum Disorder (ASD). The activity and function of PTEN in neurons have been studied extensively in various settings, whereas animal models offer the best opportunity to experimentally modify and test the role of PTEN in neuronal development in vivo. On the molecular level, PTENs importance in the mTOR pathways suppression is well-known but many other interactions have been suggested and yet others likely remain undiscovered. Therefore, to systematically explore the regulatory landscape downstream of PTEN on the genetic level, we have set out to establish a singlecell RNA sequencing (scRNA-seq) workflow to measure gene expression changes as a result of knocking out the Pten gene in vivo. We were able to collect brain tissue from four conditionally knocked out mouse samples and dissociate them into single live cells, which were subsequently flow-sorted and a scRNA-seq library was prepared. All of these steps were completed in a single day, yielding sequencing data from hundreds of cells suitable for cell clustering, cell type identification, and differential expression measurements. Having demonstrated its feasibility, this approach promises to aid hypothesis generation and discovery in mapping the function of Pten as well other genes whose regulatory networks remain to be fully defined.

neuroscience↗

bioRxiv2020

A cloud-based platform for the analysis of single cell RNA sequencing data.

MotivationSingle-cell RNA sequencing (scRNA-seq) is a recent technology that has provided many valuable biological insights. Notable uses include identifying novel cell-types, measuring the cellular response to treatment, and tracking trajectories of distinct cell lineages in time. The raw data generated in this process typically amounts to hundreds of millions of sequencing reads and requires substantial computational infrastructure for downstream analysis, a major hurdle for a biological research lab. Fortunately, the preprocessing step that converts this huge sequence data into manageable cell-specific expression profiles is standardized and can be performed in the cloud. We demonstrate how a cloud-based computational framework can be used to transform the raw data into biologically interpretable cell-type-specific information, using either 3 or 5 transcriptome libraries from 10x Genomics. The processed data which is an order of magnitude smaller in size can be easily downloaded to a laptop for customized analysis to gain deeper biological insights. ResultsWe produced an automated and easily extensible pipeline in the cloud for the analysis of single-cell RNA-seq data which provides a convenient method to handle post-processing of scRNA sequencing using next generation sequencing platforms. The basic step provides the transformation of the scRNA-seq data to cell-type-specific expression profiles and computes the quality control metrics for the dataset. The extensibility of the platform is demonstrated by adding a doublet-removal algorithm and recomputing the clustering of the cells. Any additional computational steps that take a cell-type expression counts matrix as input can be easily added to this framework with minimal effort. AvailabilityThe framework and its documentation for installation is available at the Github repository http://github.com/nj3252/CB-Source/ Contactkyun@houstonmethodist.org Supplementary informationSupplementary data available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Time-course single-cell RNA sequencing reveals transcriptional dynamics and heterogeneity of limbal stem cells derived from human pluripotent stem cells

Human pluripotent stem cell-derived limbal stem cells (hPSC-derived LSCs) provide a promising cell source for corneal transplants and ocular surface reconstruction. Although recent efforts in the identification of LSC markers have increased our understanding of the biology of LSCs, the lack of knowledge of the developmental origin, cell fate determination, and identity of human LSCs hindered the establishment of differentiation protocols for hPSC-derived LSCs and hold back their clinical application. Here, we performed a time-course single-cell RNA-seq to investigate transcriptional heterogeneity and expression changes of LSCs derived from human embryonic stem cells. Based on current protocol, expression heterogeneity of reported LSC markers were identified in subpopulations of differentiated cells. EMT has been shown to occur during differentiation process, which could possibly result in generation of untargeted cells. Pseudotime trajectory analysis revealed transcriptional changes and signatures of commitment of hPSCs-derived LSCs and their progeny - the transit amplifying cells. Furthermore, several new makers of LSCs were identified, which could facilitate elucidating the identity and developmental origin of human LSCs in vivo.

developmental biology↗

bioRxiv2020

Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses

The new coronavirus (2019-nCoV) outbreak from December 2019 in Wuhan, Hubei, China, has been declared a global public health emergency. Angiotensin I converting enzyme 2 (ACE2), is the host receptor by 2019-nCov to infect human cells. Although ACE2 is reported to be expressed in lung, liver, stomach, ileum, kidney and colon, its expressing levels are rather low, especially in the lung. 2019-nCoV may use co-receptors/auxiliary proteins as ACE2 partner to facilitate the virus entry. To identify the potential candidates, we explored the single cell gene expression atlas including 119 cell types of 13 human tissues and analyzed the single cell co-expression spectrum of 51 reported RNA virus receptors and 400 other membrane proteins. Consistent with other recent reports, we confirmed that ACE2 was mainly expressed in lung AT2, liver cholangiocyte, colon colonocytes, esophagus keratinocytes, ileum ECs, rectum ECs, stomach epithelial cells, and kidney proximal tubules. Intriguingly, we found that the candidate co-receptors, manifesting the most similar expression patterns with ACE2 across 13 human tissues, are all peptidases, including ANPEP, DPP4 and ENPEP. Among them, ANPEP and DPP4 are the known receptors for human CoVs, suggesting ENPEP as another potential receptor for human CoVs. We also conducted "CellPhoneDB" analysis to understand the cell crosstalk between CoV-targets and their surrounding cells across different tissues. We found that macrophages frequently communicate with the CoVs targets through chemokine and phagocytosis signaling, highlighting the importance of tissue macrophages in immune defense and immune pathogenesis.

immunology↗

bioRxiv2018

Single-cell RNA sequencing reveals novel cell differentiation dynamics during human airway epithelium regeneration

BackgroundIt is usually considered that the upper airway epithelium is composed of multiciliated, goblet, secretory and basal cells, which collectively constitute an efficient first line of defense against inhalation of noxious substances. Upon injury, regeneration of this epithelium through proliferation and differentiation can restore a proper mucociliary function. However, in chronic airway diseases, the injured epithelium frequently displays defective repair leading to tissue remodeling, characterized by a loss of multiciliated cells and mucus hyper-secretion. Delineating drivers of differentiation dynamics and cell fate in the human airway epithelium is important to preserve homeostasis.\n\nResultsWe have used single cell transcriptomics to characterize the sequence of cellular and molecular processes taking place during human airway epithelium regeneration. We have characterized airway subpopulations with high resolution and lineage inference algorithms have unraveled cell trajectories from basal to luminal cells, providing markers for specific cell populations, such as deuterosomal cells, i.e. precursors of multiciliated cells. We report that goblet cells, like secretory cells, can act as precursors of multiciliated cells. Our study provides a repertoire of molecules involved in key steps of the regeneration process, either keratins or components of the Notch, Wnt or BMP/TGF{beta} signaling pathways. Our findings were confirmed in independent experiments performed on fresh human and pig airway samples, and on mouse tracheal epithelial cells.\n\nConclusionsOur single-cell RNA-seq study provides novel insights about airway epithelium differentiation dynamics, clarifies cell trajectories between secretory, goblet and multiciliated cells, identifies novel cell subpopulations, and maps the activation and repression of key signaling pathways.

cell biology↗

bioRxiv2025

A mathematical framework for human neutrophil state transitions inferred from single-cell RNA sequence data

Neutrophils, the most abundant immune cells in the human circulation, play a central role in the innate immune system. While neutrophil heterogeneity is a topic of increasing research interest, few efforts have been made to model the dynamics of neutrophil population subsets. We develop a mathematical model to describe the dynamics that characterizes the states and transitions involved in the maturation of human neutrophils. We use single-cell gene expression data to identify five clusters of healthy human neutrophils, and pseudo-time analysis to inform model structure. We find that precursor neutrophils transition into immature neutrophils, which then either transition to an interferon-responsive state or continue to mature through two further states. The key model parameters are the transition rates (the inverse of a transition rate is the mean waiting time in one state before transitioning to another). In this framework, the transition from the precursor to immature state (mean time less than an hour) is more rapid than subsequent transitions (mean times more than 12 hours). Approximately a quarter of neutrophils are estimated to follow the interferon-responsive path; the remainder continue along the standard maturation pathway. We use Bayesian inference to describe the variation, between individuals, in the fraction of cells within each cluster.

cell biology↗

bioRxiv2020

Single cell RNA sequencing reveals the landscape of early female germ cell development

Female germ cell development consists of complex events including sex determination, meiosis initiation, retardation and resumption. During early oogenesis, the asynchrony of the transition from mitosis to meiosis results in heterogeneity in the female germ cell populations at a certain embryonic stage, which limits the studies of meiosis initiation and progression at a higher resolution level. Here, we investigated the transcriptional profiles of 19363 single germ cells collected from E12.5, E14.5 and E16.5 mouse fetal ovaries. Clustering analysis identified seven groups and defined dozens of corresponding transcription factors, providing a global view of cellular differentiation from primordial germ cells towards meiocytes. Further, we explored the dynamics of gene expression within the developmental trajectory with special focus on the mechanisms underlying meiotic initiation. We found that Dpy30 may be involved in the regulation of meiosis initiation at the epigenetic level. Our data provide key insights into the transcriptome features of peri-meiotic female germ cells, which offers new information not only on meiosis initiation and progression but also on screening pathogenic mutations in meiosis-associated diseases.

developmental biology↗

bioRxiv2025

CSF single-cell RNA sequencing reveals clonally expanded CD4+ stem cell-like memory T cells in GAD65-antibody associated neurological syndromes

BackgroundGlutamic acid decarboxylase (GAD) antibody-associated autoimmune neurological syndromes (AINS) are a spectrum of autoimmune-mediated CNS disorders. While antibodies targeting the 65 kDa isoform of GAD are of high diagnostic value, T cell mediated cytotoxicity has been identified as a key component of disease pathogenesis. The precise pathophysiological mechanisms by which the disease is triggered and maintained, however, remain incompletely understood. MethodsWe performed single-cell transcriptome and immune repertoire sequencing (sc-seq) in CSF and blood of 8 anti-GAD65 AINS patients compared to 8 non-inflammatory controls. Monoclonal antibodies (mAbs) were synthesized from B cell receptor (BCR) data to evaluate the B cellular immune response. FindingsWe identified an increase and expansion of activated CD4+ stem cell-like memory T cells (TSCM) in the CSF of anti-GAD65 AINS patients. Expanded T cells showed increased expression of proinflammatory genes. The mAb analysis revealed a high frequency of GAD65-reactive BCRs in the CSF of anti-GAD65 AINS patients with increased somatic hypermutations compared to non-GAD-reactive BCRs and BCRs from controls. ConclusionsSc-seq identified clonally expanded CD4+ TSCM in the CSF of anti-GAD65 AINS patients harboring cytotoxic properties likely contributing to disease pathogenesis. GAD-reactive B cells circulate in the CSF of anti-GAD65 AINS patients further supporting the concept of an antigen-specific intrathecal immune response. Future studies need to clarify the actual pathogenicity of these immune cells and the link between T and B cellular immune mechanisms in the pathogenesis of anti-GAD65 AINS. FundingGerman Research Foundation (ERARE18-202 UltraAIE), German Federal Ministry of Education and Research (CONNECT GENERATE (2.0); 01GM1908A and 01GM2208A).

immunology↗

bioRxiv2024

NK Cell Exhaustion in Wilson's Disease Revealed by Single-cell RNA Sequencing Predicts the Prognosis of Cholecystitis

Metabolic abnormalities associated with liver disease have a significant impact on the risk and prognosis of cholecystitis. However, the underlying mechanism is complicated and remains to be elucidated. In particular, the effect of metabolic abnormalities on the progression of cholecystitis through the regulation of immune cell function is poorly understood. In this study, we investigated this issue using Wilsons disease (WD) as a model. Wilsons disease is a genetic disorder characterized by impaired mitochondrial function and abnormal copper metabolism. Our retrospective clinical study of over 600 patients with WD found that they have a significantly higher incidence of cholecystitis and a poorer prognosis. The immune cell landscape in the hepatic mesenchymal stromal microenvironment of WD patients was shown using single-cell RNA. A major change is the constitution and function of the innate immune system, including enhanced antigen presentation process, activation of the immune response, and activation of lymphocytes. The proportion of mononuclear phagocytes and natural killer (NK) cells is increased, and the primary characteristic and function of macrophage, Kupffer cell, neutrophil, and NK cell are altered. Exhaustion of NK cell is the fundamental factor, supported by an increase in the expression of the inhibitory receptors KLRC1 and TIGIT and a decrease in the expression of cytotoxic molecules. Clinical tissue and blood samples verified increased KLRC1+ and TIGIT+ NK cells and decreased IFN{gamma}+ NK cells in WD. Further bioinformatic analysis has confirmed a positive correlation between NK cell exhaustion and poor prognosis in cholecystitis and other inflammatory diseases. The study demonstrated abnormal function of liver mesenchymal immune cells triggered by specific metabolic dysfunction in WD, with a focus on the correlation between NK cell exhaustion and poor healing of cholecystitis. Our findings highlight the immune cell dysfunction due to metabolic changes in hepatocytes and provide new insights into the improvement of inflammatory diseases by assessing immune cell function.

immunology↗

bioRxiv2019

Tempora: cell trajectory inference using time-series single-cell RNA sequencing data

AO_SCPCAPBSTRACTC_SCPCAPSingle-cell RNA sequencing (scRNAseq) can map cell types, states and transitions during dynamic biological processes such as development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNAseq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNAseq data. In performance comparison tests, Tempora accurately inferred developmental lineages in human skeletal myoblast differentiation and murine cerebral cortex development, beating state of the art methods. Tempora uses biological pathway information to help identify cell type relationships and can identify important time-dependent pathways to help interpret the inferred trajectory. Our results demonstrate the utility of time information to supervise trajectory inference for scRNA-seq based analysis.

bioinformatics↗

bioRxiv2020

Single cell RNA sequencing reveals differential cell cycle activity in key cell populations during nephrogenesis

The kidney is a complex organ composed of more than 30 terminally differentiated cell types that all are required to perform its numerous homeostatic functions. Defects in kidney development are a significant cause of chronic kidney disease in children, which can lead to kidney failure that can only be treated by transplant or dialysis. A better understanding of molecular mechanisms that drive kidney development is important for designing strategies to enhance renal repair and regeneration. In this study, we profiled gene expression in the developing mouse kidney at embryonic day 14.5 at single cell resolution. Consistent with previous studies, clusters with distinct transcriptional signatures clearly identify major compartments and cell types of the developing kidney. Cell cycle activity distinguishes between the "primed" and "self-renewing" sub-populations of nephron progenitors, with increased expression of the cell cycle related genes Birc5, Cdca3, Smc2 and Smc4 in "primed" nephron progenitors. Augmented Birc5 expression was also detected in immature distal tubules and a sub-set of ureteric bud cells, suggesting that Birc5 might be a novel key molecule required for early events of nephron patterning and tubular fusion between the distal nephron and the collecting duct epithelia.

genomics↗

bioRxiv2021

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry

Most high-dimensional datasets are thought to be inherently low-dimensional, that is, datapoints are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion, and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifolds embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. Keeping in mind practical constraints of real-world datasets, like small sample sizes and measurement noise, we found that estimating curvature is only feasible for even simple, low-dimensional toy manifolds, when the extrinsic approach is used. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches, and recapitulated its topological classification as a Klein bottle. Lastly, we applied the approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells, revealing for the first time the intrinsic curvature of scRNAseq manifolds.

bioinformatics↗

bioRxiv2021

Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures

Plasmodium vivax sporozoites reside in the salivary glands of a mosquito before infecting a human host. Previous transcriptome-wide studies in populations of these forms were limited in their ability to elucidate cell-to-cell variation, thereby masking cellular states potentially important in understanding transmission outcomes. In this study, we performed transcription profiling on 9,947 P. vivax sporozoites to assess the extent to which they differ at single-cell resolution. We show that sporozoites residing in the mosquitos salivary glands exist in distinct developmental states, as defined by their transcriptomic signatures. Additionally, relative to P. falciparum, P. vivax displays overlapping and unique gene usage patterns, highlighting conserved and species-specific gene programs. Notably, distinguishing P. vivax from P. falciparum were a subset of P. vivax sporozoites expressing genes associated with translational regulation and repression. Finally, our comparison of single-cell transcriptomic data from P. vivax sporozoite and erythrocytic forms reveals gene usage patterns unique to sporozoites. In defining the transcriptomic signatures of individual P. vivax sporozoites, our work provides new insights into the factors driving their developmental trajectory and lays the groundwork for a more comprehensive P. vivax cell atlas.

genomics↗