Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors.

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

A contamination focused approach for optimizing the single-cell RNA-seq experiment.

Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.

Matrix stiffness enhances cancer-macrophage interactions and M2-like macrophage accumulation in the breast tumor microenvironment.

The role of intratumor heterogeneity is becoming increasingly apparent in part due to expansion in single cell technologies. Clinically, tumor heterogeneity poses several obstacles to effective cancer therapy dealing with biomarker variability and treatment responses. Matrix stiffening is known to occur during tumor progression and contribute to pathogenesis in several cancer hallmarks, including tumor angiogenesis and metastasis. However, the effects of matrix stiffening on intratumor heterogeneity have not been thoroughly studied. In this study, we applied single-cell RNA sequencing to investigate the differences in the transcriptional landscapes between stiff and compliant MMTV-PyMT mouse mammary tumors. We found similar compositions of cancer and stromal subpopulations in compliant and stiff tumors but differential intercellular communication and a significantly higher concentration of tumor-promoting, M2-like macrophages in the stiffer tumor microenvironments. Interestingly, we found that cancer cells seeded on stiffer substrates recruited more macrophages. Furthermore, elevated matrix stiffness increased Colony Stimulating Factor 1 (CSF-1) expression in breast cancer cells and reduction of CSF-1 expression on stiffer substrates reduced macrophage recruitment. Thus, our results demonstrate that tissue phenotypes were conserved between stiff and compliant tumors but matrix stiffening altered cell-cell interactions which may be responsible for shifting the phenotypic balance of macrophages residing in the tumor microenvironment towards a pro-tumor progression M2 phenotype. STATEMENT OF SIGNIFICANCE: Cells within tumors are highly heterogeneous, posing challenges with treatment and recurrence. While increased tissue stiffness can promote several hallmarks of cancer, its effects on tumor heterogeneity are unclear. We used single-cell RNA sequencing to investigate the differences in the transcriptional landscapes between stiff and compliant MMTV-PyMT mouse mammary tumors. We found similar compositions of cancer and stromal subpopulations in compliant and stiff tumors but differential intercellular communication and a significantly higher concentration of tumor-promoting, M2-like macrophages in the stiffer tumor microenvironments. Using a biomaterial-based platform, we found that cancer cells seeded on stiffer substrates recruited more macrophages, supporting our in vivo findings. Together, our results demonstrate a key role of matrix stiffness in affecting cell-cell communication and macrophage recruitment.

Health Risks and Musculoskeletal Problems of Elite Mobile Esports Players: a Cross-Sectional Descriptive Study.

BACKGROUND: Mobile-gaming athletes sit in the same posture for prolonged periods, contributing to significant health risks. This study investigated the health profiles, fatigue, pain and complaints, and musculoskeletal problems of full-time mobile-gaming athletes. METHODS: A total of 50 elite mobile-gaming athletes were involved in this study. They were the starting lineup players from all ten professional teams competing in a top-tier multiplayer online battle arena tournament. A survey was conducted to evaluate their fatigue patterns, pain levels, and complaints. A descriptive analysis was conducted to evaluate the athletes' health profiles [body mass index (BMI), fat ratio], fatigue, number of complaints, and musculoskeletal problems. The associations of career duration with BMI, fat ratio, and the total number of confirmed injuries were then determined using Spearman's rank correlation test. RESULTS: A total of 46% and 44% of the participants felt tired frequently and occasionally, while 34% and 58% experienced eyestrain frequently and occasionally, respectively. More than 30% of the participants reported headache and rhinitis. A longer esports career duration was associated with a reduction in BMI (r = -0.272, p = 0.056). Career duration had no significant association with smoking habits (p = 0.666), alcohol habits (p = 0.655), coffee habits (p = 0.946), rounds of games for which the player could maintain concentration (p = 0.253), ease of eyestrain (p = 0.569), tiredness (p = 0.510), dizziness (p = 0.071), or leg numbness (p = 0.318). CONCLUSION: The findings of this study stress the significance of esports injuries and indicate preventive measures for both athletes and recreational players.

Spine Posture, Mobility, and Stability of Top Mobile Esports Athletes: A Case Series.

Professional esports athletes spend a long time in the same sitting posture during training and competition. Mobile esports may exacerbate potential postural problems because of the closer and unsupported arms and because athletes spend more time in a forward-/flexed-head posture. Prolonged sitting in these postures carries significant health risks and may lead to musculoskeletal problems and injuries. The objective of this retrospective study is to assess the posture, mobility, and stability of the spine for professional mobile esports athletes. We collected spine-assessment data from 48 athletes participating in a top-tier league on a real-time-strategy battle-arena online game. The spinal assessment was conducted using the SpinalMouse® under upright standing and trunk flexion in addition to the Matthiass test. Measurements were converted into Idiag Scores by the SpinalMouse® software. The Idiag Posture, Idiag Mobility, and Idiag Stability scores were 62.50 (IQR: 21), 63.50 (IQR: 19.5), and 54.50 (IQR: 14.5), respectively, and were significantly lower (p < 0.001) than the reference normative value (100). Age was found to have a weak positive correlation with the posture score (ρ = 0.29, p = 0.048). Although career duration appeared to lower the scores, the association was insignificant (p > 0.05). The scores also had no significant association with body height, body mass, body mass index, and esports team (p > 0.05). It was anticipated that mobile-based esports would attenuate the biomechanics of the spine and increase the likelihood of musculoskeletal problems, such as neck and back pain.

MTG16 regulates colonic epithelial differentiation, colitis, and tumorigenesis by repressing E protein transcription factors.

Aberrant epithelial differentiation and regeneration contribute to colon pathologies, including inflammatory bowel disease (IBD) and colitis-associated cancer (CAC). Myeloid translocation gene 16 (MTG16, also known as CBFA2T3) is a transcriptional corepressor expressed in the colonic epithelium. MTG16 deficiency in mice exacerbates colitis and increases tumor burden in CAC, though the underlying mechanisms remain unclear. Here, we identified MTG16 as a central mediator of epithelial differentiation, promoting goblet and restraining enteroendocrine cell development in homeostasis and enabling regeneration following dextran sulfate sodium-induced (DSS-induced) colitis. Transcriptomic analyses implicated increased Ephrussi box-binding transcription factor (E protein) activity in MTG16-deficient colon crypts. Using a mouse model with a point mutation that attenuates MTG16:E protein interactions (Mtg16P209T), we showed that MTG16 exerts control over colonic epithelial differentiation and regeneration by repressing E protein-mediated transcription. Mimicking murine colitis, MTG16 expression was increased in biopsies from patients with active IBD compared with unaffected controls. Finally, uncoupling MTG16:E protein interactions partially phenocopied the enhanced tumorigenicity of Mtg16-/- colon in the azoxymethane/DSS-induced model of CAC, indicating that MTG16 protects from tumorigenesis through additional mechanisms. Collectively, our results demonstrate that MTG16, via its repression of E protein targets, is a key regulator of cell fate decisions during colon homeostasis, colitis, and cancer.

Single-Cell Transcriptomics Reveals a Conserved Metaplasia Program in Pancreatic Injury.

BACKGROUND & AIMS: Acinar to ductal metaplasia (ADM) occurs in the pancreas in response to tissue injury and is a potential precursor for adenocarcinoma. The goal of these studies was to define the populations arising from ADM, the associated transcriptional changes, and markers of disease progression. METHODS: Acinar cells were lineage-traced with enhanced yellow fluorescent protein (EYFP) to follow their fate post-injury. Transcripts of more than 13,000 EYFP+ cells were determined using single-cell RNA sequencing (scRNA-seq). Developmental trajectories were generated. Data were compared with gastric metaplasia, KrasG12D-induced neoplasia, and human pancreatitis. Results were confirmed by immunostaining and electron microscopy. KrasG12D was expressed in injury-induced ADM using several inducible Cre drivers. Surgical specimens of chronic pancreatitis from 15 patients were evaluated by immunostaining. RESULTS: scRNA-seq of ADM revealed emergence of a mucin/ductal population resembling gastric pyloric metaplasia. Lineage trajectories suggest that some pyloric metaplasia cells can generate tuft and enteroendocrine cells (EECs). Comparison with KrasG12D-induced ADM identifies populations associated with disease progression. Activation of KrasG12D expression in HNF1B+ or POU2F3+ ADM populations leads to neoplastic transformation and formation of MUC5AC+ gastric-pit-like cells. Human pancreatitis samples also harbor pyloric metaplasia with a similar transcriptional phenotype. CONCLUSIONS: Under conditions of chronic injury, acinar cells undergo a pyloric-type metaplasia to mucinous progenitor-like populations, which seed disparate tuft cell and EEC lineages. ADM-derived EEC subtypes are diverse. KrasG12D expression is sufficient to drive neoplasia when targeted to injury-induced ADM populations and offers an alternative origin for tumorigenesis. This program is conserved in human pancreatitis, providing insight into early events in pancreas diseases.

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps.

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

Deciphering the cancer microenvironment from bulk data with EcoTyper.

In this issue of Cell, Luca, Steen et al. develop the EcoTyper software to deconvolve tumor-microenvironment interactions from high volume bulk transcriptomics data. They demonstrate its effectiveness in improving predictions for tumor progression and patient prognosis for a variety of tumor types from multiple data sources.

Measuring Collaboration Through Concurrent Electronic Health Record Usage: Network Analysis Study.

BACKGROUND: Collaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods. OBJECTIVE: We aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage. METHODS: By extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center's neonatal intensive care unit (NICU) to validate the effectiveness of our framework. RESULTS: There was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters. CONCLUSIONS: Leveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.

Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools.

Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as "one-stop shops" for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems. For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority. Here, we demonstrate a full data analysis pipeline, constructed modularly from open-source software, including tools that we have contributed. For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018), Heiser et al. (2020), and Heiser and Lau (2020).

Automated quality control and cell identification of droplet-based single-cell data using dropkick.

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in data sets with disparate library sizes confounded by high technical noise (i.e., batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining data set-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against conventional thresholding approaches and EmptyDrops, a popular computational method, showing greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low- and high-background data sets that dropkick's weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to data set-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell Python packages.

Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning.

OBJECTIVE: The characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics. MATERIALS AND METHODS: We leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit. RESULTS: We examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57 234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness. CONCLUSIONS: The use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.

Succinate Produced by Intestinal Microbes Promotes Specification of Tuft Cells to Suppress Ileal Inflammation.

BACKGROUND & AIMS: Countries endemic for parasitic infestations have a lower incidence of Crohn's disease (CD) than nonendemic countries, and there have been anecdotal reports of the beneficial effects of helminths in CD patients. Tuft cells in the small intestine sense and direct the immune response against eukaryotic parasites. We investigated the activities of tuft cells in patients with CD and mouse models of intestinal inflammation. METHODS: We used microscopy to quantify tuft cells in intestinal specimens from patients with ileal CD (n = 19), healthy individuals (n = 14), and TNFΔARE/+ mice, which develop Crohn's-like ileitis. We performed single-cell RNA sequencing, mass spectrometry, and microbiome profiling of intestinal tissues from wild-type and Atoh1-knockout mice, which have expansion of tuft cells, to study interactions between microbes and tuft cell populations. We assessed microbe dependence of tuft cell populations using microbiome depletion, organoids, and microbe transplant experiments. We used multiplex imaging and cytokine assays to assess alterations in inflammatory response following expansion of tuft cells with succinate administration in TNFΔARE/+ and anti-CD3E CD mouse models. RESULTS: Inflamed ileal tissues from patients and mice had reduced numbers of tuft cells, compared with healthy individuals or wild-type mice. Expansion of tuft cells was associated with increased expression of genes that regulate the tricarboxylic acid cycle, which resulted from microbe production of the metabolite succinate. Experiments in which we manipulated the intestinal microbiota of mice revealed the existence of an ATOH1-independent population of tuft cells that was sensitive to metabolites produced by microbes. Administration of succinate to mice expanded tuft cells and reduced intestinal inflammation in TNFΔARE/+ mice and anti-CD3E-treated mice, increased GATA3+ cells and type 2 cytokines (IL22, IL25, IL13), and decreased RORGT+ cells and type 17 cytokines (IL23) in a tuft cell-dependent manner. CONCLUSIONS: We found that tuft cell expansion reduced chronic intestinal inflammation in mice. Strategies to expand tuft cells might be developed for treatment of CD.

Dual indexed library design enables compatibility of in-Drop single-cell RNA-sequencing with exAMP chemistry sequencing platforms.

BACKGROUND: The increasing demand of single-cell RNA-sequencing (scRNA-seq) experiments, such as the number of experiments and cells queried per experiment, necessitates higher sequencing depth coupled to high data quality. New high-throughput sequencers, such as the Illumina NovaSeq 6000, enables this demand to be filled in a cost-effective manner. However, current scRNA-seq library designs present compatibility challenges with newer sequencing technologies, such as index-hopping, and their ability to generate high quality data has yet to be systematically evaluated. RESULTS: Here, we engineered a dual-indexed library structure, called TruDrop, on top of the inDrop scRNA-seq platform to solve these compatibility challenges, such that TruDrop libraries and standard Illumina libraries can be sequenced alongside each other on the NovaSeq. On scRNA-seq libraries, we implemented a previously-documented countermeasure to the well-described problem of index-hopping, demonstrated significant improvements in base-calling accuracy on the NovaSeq, and provided an example of multiplexing twenty-four scRNA-seq libraries simultaneously. We showed favorable comparisons in transcriptional diversity of TruDrop compared with prior inDrop libraries. CONCLUSIONS: Our approach enables cost-effective, high throughput generation of sequencing data with high quality, which should enable more routine use of scRNA-seq technologies.

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features.

Single cells are the building blocks of tissue systems that determine organ phenotypes, behaviors, and functions. Understanding the differences between cell types and their activities might provide us with insights into normal tissue physiology, development of disease, and new therapeutic strategies. Although -omic level single-cell technologies are a relatively recent development that have been used only in research settings, these approaches might eventually be used in the clinic. We review the prospects of applying single-cell genome, transcriptome, epigenome, proteome, and metabolome analyses to gastroenterology and hepatology research. Combining data from multi-omic platforms coupled to rapid technological development could lead to new diagnostic, prognostic, and therapeutic approaches.

Coregulator Sin3a Promotes Postnatal Murine ß-Cell Fitness by Regulating Genes in Ca2+ Homeostasis, Cell Survival, Vesicle Biosynthesis, Glucose Metabolism, and Stress Response.

Swi-independent 3a and 3b (Sin3a and Sin3b) are paralogous transcriptional coregulators that direct cellular differentiation, survival, and function. Here, we report that mouse Sin3a and Sin3b are coproduced in most pancreatic cells during embryogenesis but become much more enriched in endocrine cells in adults, implying continued essential roles in mature endocrine cell function. Mice with loss of Sin3a in endocrine progenitors were normal during early postnatal stages but gradually developed diabetes before weaning. These physiological defects were preceded by the compromised survival, insulin-vesicle packaging, insulin secretion, and nutrient-induced Ca2+ influx of Sin3a-deficient ß-cells. RNA sequencing coupled with candidate chromatin immunoprecipitation assays revealed several genes that could be directly regulated by Sin3a in ß-cells, which modulate Ca2+/ion transport, cell survival, vesicle/membrane trafficking, glucose metabolism, and stress responses. Finally, mice with loss of both Sin3a and Sin3b in multipotent embryonic pancreatic progenitors had significantly reduced islet cell mass at birth, caused by decreased endocrine progenitor production and increased ß-cell death. These findings highlight the stage-specific requirements for the presumed "general" coregulators Sin3a and Sin3b in islet ß-cells, with Sin3a being dispensable for differentiation but required for postnatal function and survival.

ATAC-Me Captures Prolonged DNA Methylation of Dynamic Chromatin Accessibility Loci during Cell Fate Transitions.

DNA methylation of enhancers is dynamic, cell-type specific, and vital for cell fate progression. However, current models inadequately define its role within the hierarchy of gene regulation. Analysis of independent datasets shows an unanticipated overlap between DNA methylation and chromatin accessibility at enhancers of steady-state stem cells, suggesting that these two opposing features might exist concurrently. To define their temporal relationship, we developed ATAC-Me, which probes accessibility and methylation from single DNA library preparations. We identified waves of accessibility occurring rapidly across thousands of myeloid enhancers in a monocyte-to-macrophage cell fate model. Prolonged methylation states were observed at a majority of these sites, while transcription of nearby genes tracked closely with accessibility. ATAC-Me uncovers a significant disconnect between chromatin accessibility, DNA methylation status, and gene activity. This unexpected observation highlights the value of ATAC-Me in constructing precise molecular timelines for understanding the role of DNA methylation in gene regulation.

pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.

MOTIVATION: The emergence of single-cell RNA-sequencing has enabled analyses that leverage transitioning cell states to reconstruct pseudotemporal trajectories. Multidimensional data sparsity, zero inflation and technical variation necessitate the selection of high-quality features that feed downstream analyses. Despite the development of numerous algorithms for the unsupervised selection of biologically relevant features, their differential performance remains largely unaddressed. RESULTS: We implemented the neighborhood variance ratio (NVR) feature selection approach as a Python package with substantial improvements in performance. In comparing NVR with multiple unsupervised algorithms such as dpFeature, we observed striking differences in features selected. We present evidence that quantifiable dataset properties have observable and predictable effects on the performance of these algorithms. AVAILABILITY AND IMPLEMENTATION: pyNVR is freely available at https://github.com/KenLauLab/NVR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative assessment of cell population diversity in single-cell landscapes.

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.