Search

Multiple pancreatic islet single-cell RNA sequencing (scRNA-seq) datasets have been generated to study development, homeostasis, and diabetes. However, there is no consensus on cell states and pathways across conditions as well as the value of preclinical mouse models. Since these challenges can only be resolved by jointly analyzing multiple datasets, we present a scRNA-seq cross-condition mouse islet atlas (MIA). We integrated over 300,000 cells from nine datasets with 56 samples, varying in age, sex, and diabetes models, including an autoimmune type 1 diabetes (T1D) model (NOD), a gluco-/lipotoxicity T2D model (db/db), and a chemical streptozotocin (STZ) β-cell ablation model. MIA is a curated resource for interactive exploration and computational querying, providing new insights inaccessible from individual datasets. The β-cell landscape of MIA revealed new disease progression cell states and cross-publication differences between previously suggested marker genes. We show that in the STZ model β-cells transcriptionally correlate to human T2D and mouse db/db, but are less similar to human T1D and mouse NOD. We observe different pathways shared between immature, aged, and diabetes model β-cells. In conclusion, our work presents the first comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation, and demise.

The increasing generation of population-level single-cell atlases with hundreds or thousands of samples has the potential to link demographic and technical metadata with high-resolution cellular and tissue data in homeostasis and disease. Constructing such comprehensive references requires large-scale integration of heterogeneous cohorts with varying metadata capturing demographic and technical information. Here, we presentsingle-cell population level integration (scPoli), a semi-supervised conditional deep generative model for data integration, label transfer and query-to-reference mapping. Unlike other models, scPoli learns both sample and cell representations, is aware of cell-type annotations and can integrate and annotate newly generated query datasets while providing an uncertainty mechanism to identify unknown populations. We extensively evaluated the method and showed its advantages over existing approaches. We applied scPoli to two population-level atlases of lung and peripheral blood mononuclear cells (PBMCs), the latter consisting of roughly 8 million cells across 2,375 samples. We demonstrate that scPoli allows atlas-level integration and automatic reference mapping with label transfer. It can explain sample-level biological and technical variations such as disease, anatomical location and assay by means of its novel sample embeddings. We use these embeddings to explore sample-level metadata, enable automatic sample classification and guide a data integration workflow. scPoli also enables simultaneous sample-level and cell-level analysis of gene expression patterns, revealing genes associated with batch effects and the main axes of between-sample variation. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

Study Design Retrospective cohort study. Objectives This study aimed to (1) evaluate for any temporal trends in the rates of VTE, deep venous thrombosis (DVT), pulmonary embolism (PE), and mortality from 2011 to 2020 and (2) identify the predictors of VTE following lumbar fusion surgery. Methods Annual incidences of 30-day VTE, DVT, PE, and mortality were calculated for each of the operation year groups from 2011 to 2020. Multivariable Poisson regression was utilized to test the association between operation year and primary outcomes, as well as to identify significant predictors of VTE. Results A total of 121,205 patients were included. There were no statistically significant differences in VTE, DVT, PE, or mortality rates among the operation year groups. Multivariable regression analysis revealed that compared to 2011, operation year 2019 was associated with significantly lower rates of DVT. Age, BMI, prolonged operation time, prolonged length of stay, non-home discharge, anterior fusion, smoking status, functional dependence, and chronic steroid use were identified as independent predictors of VTE following lumbar fusion. Female sex, Hispanic ethnicity, and outpatient surgery setting were identified as protective factors from VTE in this cohort. Conclusions Rates of VTE after lumbar fusion have remained mostly unchanged between 2011 and 2020. Older age, higher BMI, longer operation time, prolonged length of stay, non-home discharge, anterior fusion, smoking, functional dependence, and steroid use were independent predictors of VTE after lumbar fusion, while female sex, Hispanic ethnicity, and outpatient surgery were the protective factors.

Background: This study aimed to examine the relationship between anatomical surface landmarks in fresh frozen cadavers as related to in vivo endoscopic trigger finger release (ETFR) and present clinical outcomes after a single-portal antegrade ETFR technique. Methods: Endoscopic trigger finger release was performed on 40 cadaveric digits. Each digit was dissected and the following measurements were recorded: distance from palmar digital crease and A1 pulley, length of the A1 pulley, percentage of A1 pulley released, and injury to vulnerable anatomy. A retrospective chart review was performed on 48 patients (62 digits) treated with ETFR. Outcome measures included grip and pinch strength, range of motion, Disability of Arm, Shoulder, and Hand (DASH) questionnaires, and Visual Analog Scale (VAS) pain scores. Results: Release of the A1 pulley was achieved in 33 of the 40 cadaveric digits (83%) with an A2 pulley laceration rate of 25%. No flexor tendon or neurovascular injuries occurred. Gross grasp, lateral pinch, 3-jaw chuck, and precision pinch strength had 85%, 90%, 82%, and 90% recovery, respectively. At the final follow-up, average metacarpophalangeal joint, proximal interphalangeal joint, and distal interphalangeal joint range of motion were within the normal limits. Mean VAS scores decreased from 5.7 preoperatively to 1.0 postoperatively and mean DASH score at the final follow-up was 4.8. Conclusions: With the use of anatomical surface landmarks, ETFR may be performed in an efficient and reproducible manner. Patients treated with ETFR had low complication rates, good functional recovery, and improved pain at short-term follow-up. Further study of long-term outcomes and cost-effectiveness of ETFR is warranted.

Population-scale single-cell RNA sequencing (scRNA-seq) is now viable, enabling finer resolution functional genomics studies and leading to a rush to adapt bulk methods and develop new single-cell-specific methods to perform these studies. Simulations are useful for developing, testing, and benchmarking methods but current scRNA-seq simulation frameworks do not simulate population-scale data with genetic effects. Here, we present splatPop, a model for flexible, reproducible, and well-documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci. splatPop can also simulate complex batch, cell group, and conditional effects between individuals from different cohorts as well as genetically-driven co-expression. Supplementary Information The online version contains supplementary material available at (10.1186/s13059-021-02546-1).

Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for cell type classification tailored to datasets annotated at different levels of coarseness. We demonstrate the utility of sfaira by training models across anatomic data partitions on 8 million cells. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02452-6.

Recent years have seen a revolution in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq). As the number, size and complexity of scRNA-seq datasets continue to increase, so does the number of computational methods and software tools for extracting meaning from them. Since 2016 the scRNA-tools database has catalogued software tools for analysing scRNA-seq data. With the number of tools in the database passing 1000, we take this opportunity to provide an update on the state of the project and the field. Analysis of five years of analysis tool tracking data clearly shows the evolution of the field, and that the focus of developers has moved from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find evidence that open science practices reward developers with increased recognition and help accelerate the field.

With improving technology and decreasing costs, single-cell RNA sequencing (scRNA-seq) at the population scale has become more viable, opening up the doors to study functional genomics at the single-cell level. This development has lead to a rush to adapt bulk methods and develop new single-cell-specific methods and tools for computational analysis of these studies. Many single-cell methods have been tested, developed, and benchmarked using simulated data. However, current scRNA-seq simulation frameworks do not allow for the simulation of population-scale scRNA-seq data. Here, we present splatPop, a new Splatter model, for flexible, reproducible, and well documented simulation of population-scale scRNA-seq data with known expression quantitative trait loci (eQTL) effects. The splatPop model also allows for the simulation of complex batch effects, cell group effects, and conditional effects between individuals from different cohorts.

Exploratory analysis of single-cell RNA-seq data sets is currently based on statistical and machine learning models that are adapted to each new data set from scratch. A typical analysis workflow includes a choice of dimensionality reduction, selection of clustering parameters, and mapping of prior annotation. These steps typically require several iterations and can take up significant time in many single-cell RNA-seq projects. Here, we introduce sfaira, which is a single-cell data and model zoo which houses data sets as well as pre-trained models. The data zoo is designed to facilitate the fast and easy contribution of data sets, interfacing to a large community of data providers. Sfaira currently includes 233 data sets across 45 organs and 3.1 million cells in both human and mouse. Using these data sets we have trained eight different example model classes, such as autoencoders and logistic cell type predictors: The infrastructure of sfaira is model agnostic and allows training und usage of many previously published models. Sfaira directly aids in exploratory data analysis by replacing embedding and cell type annotation workflows with end-to-end pre-trained parametric models. As further example use cases for sfaira, we demonstrate the extraction of gene-centric data statistics across many tissues, improved usage of cell type labels at different levels of coarseness, and an application for learning interpretable models through data regularization on extremely diverse data sets.

Cell atlases often include samples that span locations, labs, and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration.Choosing a data integration method is a challenge due to the difficulty of defining integration success. Here, we benchmark 38 method and preprocessing combinations on 77 batches of gene expression, chromatin accessibility, and simulation data from 23 publications, altogether representing >1.2 million cells distributed in nine atlas-level integration tasks. Our integration tasks span several common sources of variation such as individuals, species, and experimental labs. We evaluate methods according to scalability, usability, and their ability to remove batch effects while retaining biological variation.Using 14 evaluation metrics, we find that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, BBKNN, Scanorama, and scVI perform well, particularly on complex integration tasks; Seurat v3 performs well on simpler tasks with distinct biological signals; and methods that prioritize batch removal perform best for ATAC-seq data integration. Our freely available reproducible python module can be used to identify optimal data integration methods for new data, benchmark new methods, and improve method development.

Background: Open trigger finger release (OTFR) and endoscopic trigger finger release (ETFR) are effective methods in treating stenosing tenosynovitis. However, a paucity of literature exists comparing the techniques. This study describes and compares postoperative complications following OTFR and ETFR at a single institution. Methods: Patients undergoing trigger finger release between 2018 and 2020 within a single institution were identified. Electronic medical records were reviewed for patient demographics, surgical history, surgical characteristics, and clinical outcomes. Major and minor postoperative complications were assessed. Secondary outcome measures included tourniquet time and procedure time. Statistical analysis evaluated associations between postoperative complications, surgical technique, patient demographics, and surgical characteristics. Results: In total, 57 patients (80 digits) were included in the study: 42 digits treated with OTFR and 38 digits treated with ETFR. Mean follow-up time was 57.6 ± 69.0 days (range, 7-307 days) for ETFR and 34.2 ± 26.3 days (range, 6-120 days) for OTFR. Overall, major, and minor complication rates for the cohort were 8.8%, 1.8% and 7.0%, respectively. There were no major complications following ETFR and 1 following OTFR (4%), the isolated case being postoperative Chronic regional pain syndrome. Minor complication rates were similar following OTFR (8%) and ETFR (6%). Persistent digit stiffness and swelling were found to be the most prevalent minor complications (n = 2, respectively), followed by wound dehiscence (n = 1). Female patients were significantly more likely to experience postoperative complications. Conclusions: Major complications following trigger finger release are unlikely; however, minor complications are prominent. Patients treated with OTFR and ETFR showed similar postoperative complication rates. Continued investigations into the benefits of ETFR are warranted.

The utility of human pluripotent stem cell–derived kidney organoids relies implicitly on the robustness and transferability of the protocol. Here we analyze the sources of transcriptional variation in a specific kidney organoid protocol. Although individual organoids within a differentiation batch showed strong transcriptional correlation, we noted significant variation between experimental batches, particularly in genes associated with temporal maturation. Single-cell profiling revealed shifts in nephron patterning and proportions of component cells. Distinct induced pluripotent stem cell clones showed congruent transcriptional programs, with interexperimental and interclonal variation also strongly associated with nephron patterning. Epithelial cells isolated from organoids aligned with total organoids at the same day of differentiation, again implicating relative maturation as a confounder. This understanding of experimental variation facilitated an optimized analysis of organoid-based disease modeling, thereby increasing the utility of kidney organoids for personalized medicine and functional genomics.

RNA sequencing of individual cells allows us to take a snapshot of the dynamic processes within a cell and explore differences between cell types. As this technology has developed over the last few years it has been rapidly adopted by researchers in areas such as developmental biology, and many single-cell RNA sequencing datasets are now available. Coinciding with the development of protocols for producing single-cell RNA sequencing data there has been a simultaneous burst in the development of computational analysis methods. My thesis explores the computational tools and techniques for analysing single-cell RNA sequencing data. I present a database that charts the release of analysis software, where it has been published and what it can be used for, as well as a website that makes this information publicly available. I also present two of my own tools and techniques including Splatter, a software package for easily simulating single-cell datasets from multiple models, and clustering trees, a visualisation approach for inspecting clustering at multiple resolutions. In the final part of my thesis I perform analysis of a dataset from kidney organoids to demonstrate and compare some current analysis methods. Taken together, my thesis covers many aspects of the tools and techniques for single-cell RNA sequencing by describing the approaches that are available, presenting software that can help in developing and evaluating methods, introducing an approach for aiding one of the most common analysis tasks, and showing how tools can be used to extract meaning from a real dataset.

Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.

Airway epithelial cells and macrophages differ markedly in their responses to influenza A virus (IAV) infection. To investigate transcriptional responses underlying these differences, purified subsets of type II airway epithelial cells (ATII) and alveolar macrophages (AM) recovered from the lungs of mock- or IAV-infected mice at 9 h postinfection were subjected to RNA sequencing. This time point was chosen to allow for characterization of cell types first infected with the virus inoculum, prior to multicycle virus replication and the infiltration of inflammatory cells into the airways. In the absence of infection, AM predominantly expressed genes related to immunity, whereas ATII expressed genes consistent with their physiological roles in the lung. Following IAV infection, AM almost exclusively activated cell-intrinsic antiviral pathways that were dependent on interferon (IFN) regulatory factor 3/7 (IRF3/7) and/or type I IFN signaling. In contrast, IAV-infected ATII activated a broader range of physiological responses, including cell-intrinsic antiviral pathways, which were both independent of and dependent on IRF3/7 and/or type I IFN. These data suggest that transcriptional profiles hardwired during development are a major determinant underlying the different responses of ATII and AM to IAV infection. IMPORTANCE Airway epithelial cells (AEC) and airway macrophages (AM) represent major targets of influenza A virus (IAV) infection in the lung, yet the two cell types respond very differently to IAV infection. We have used RNA sequencing to define the host transcriptional responses in each cell type under steady-state conditions as well as following IAV infection. To do this, different cell subsets isolated from the lungs of mock- and IAV-infected mice were subjected to RNA sequencing. Under steady-state conditions, AM and AEC express distinct transcriptional activities, consistent with distinct physiological roles in the airways. Not surprisingly, these cells also exhibited major differences in transcriptional responses following IAV infection. These studies shed light on how the different transcriptional architectures of airway cells from two different lineages drive transcriptional responses to IAV infection.

Kidney organoids have potential uses in disease modelling, drug screening and regenerative medicine. However, novel cost-effective techniques are needed to enable scaled-up production of kidney cell types in vitro. We describe here a modified suspension culture method for the generation of kidney micro-organoids from human pluripotent stem cells. Optimisation of differentiation conditions allowed the formation of micro-organoids, each containing six to ten nephrons that were surrounded by endothelial and stromal populations. Single cell transcriptional profiling confirmed the presence and transcriptional equivalence of all anticipated renal cell types consistent with a previous organoid culture method. This suspension culture micro-organoid methodology resulted in a three- to fourfold increase in final cell yield compared with static culture, thereby representing an economical approach to the production of kidney cells for various biological applications., Summary: A modified culture format offers a simple and cost-effective method for expansion of hPSC-derived kidney cells, facilitating scale-up of kidney cell types in vitro for biomedical applications.

We report here the complete genome sequence of Pseudomonas sp. strain NC02, isolated from soil in eastern Massachusetts. We assembled PacBio reads into a single closed contig with 132× mean coverage and then polished this contig using Illumina MiSeq reads, yielding a 6,890,566-bp sequence with 61.1% GC content.

Recent advances in our capacity to differentiate human pluripotent stem cells to human kidney tissue are moving the field closer to novel approaches for renal replacement. Such protocols have relied upon our current understanding of the molecular basis of mammalian kidney morphogenesis. To date this has depended upon population based-profiling of non-homogenous cellular compartments. In order to improve our resolution of individual cell transcriptional profiles during kidney morphogenesis, we have performed 10x Chromium single cell RNA-seq on over 6000 cells from the E18.5 developing mouse kidney, as well as more than 7000 cells from human iPSC-derived kidney organoids. We identified 16 clusters of cells representing all major cell lineages in the E18.5 mouse kidney. The differentially expressed genes from individual murine clusters were then used to guide the classification of 16 cell clusters within human kidney organoids, revealing the presence of distinguishable stromal, endothelial, nephron, podocyte and nephron progenitor populations. Despite the congruence between developing mouse and human organoid, our analysis suggested limited nephron maturation and the presence of ‘off target’ populations in human kidney organoids, including unidentified stromal populations and evidence of neural clusters. This may reflect unique human kidney populations, mixed cultures or aberrant differentiation in vitro. Analysis of clusters within the mouse data revealed novel insights into progenitor maintenance and cellular maturation in the major renal lineages and will serve as a roadmap to refine directed differentiation approaches in human iPSC-derived kidney organoids.

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time., Author summary In recent years single-cell RNA-sequencing technologies have emerged that allow scientists to measure the activity of genes in thousands of individual cells simultaneously. This means we can start to look at what each cell in a sample is doing instead of considering an average across all cells in a sample, as was the case with older technologies. However, while access to this kind of data presents a wealth of opportunities it comes with a new set of challenges. Researchers across the world have developed new methods and software tools to make the most of these datasets but the field is moving at such a rapid pace it is difficult to keep up with what is currently available. To make this easier we have developed the scRNA-tools database and website (www.scRNA-tools.org). Our database catalogues analysis tools, recording the tasks they can be used for, where they can be downloaded from and the publications that describe how they work. By looking at this database we can see that developers have focused on methods specific to single-cell data and that they embrace an open-source approach with permissive licensing, sharing of code and release of preprint publications.

Objective Although counting of circulating tumour cells (CTC) has attracted a broad interest as potential markers of tumour progression and treatment response, the lack of functional characterisation of these cells had become a bottleneck in taking these observations to the clinic. Our objective was to culture these cells in order to understand them and exploit their therapeutic potential to the full. Design Here, hypothesising that some CTC potentially have cancer stem cell (CSC) phenotype, we generated several CTC lines from the blood of patients with advanced metastatic colorectal cancer (CRC) based on their self-renewal abilities. Multiple standard tests were then employed to characterise these cells. Results Our CTC lines self-renew, express CSC markers and have multilineage differentiation ability, both in vitro and in vivo. Patient-derived CTC lines are tumorigenic in subcutaneous xenografts and are also able to colonise the liver after intrasplenic injection. RNA sequencing analyses strikingly demonstrate that drug metabolising pathways represent the most upregulated feature among CTC lines in comparison with primary CRC cells grown under similar conditions. This result is corroborated by the high resistance of the CTC lines to conventional cytotoxic compounds. Conclusions Taken together, our results directly demonstrate the existence of patient-derived colorectal CTCs that bear all the functional attributes of CSCs. The CTC culture model described here is simple and takes

Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.