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An important task in the analysis of spatially resolved transcriptomics data is to identify spatially variable genes (SVGs), or genes that vary in a 2D space. Current approaches rank SVGs based on either p-values or an effect size, such as the proportion of spatial variance. However, previous work in the analysis of RNA-sequencing identified a technical bias, referred to as the "mean-variance relationship", where highly expressed genes are more likely to have a higher variance. Here, we demonstrate the mean-variance relationship in spatial transcriptomics data. Furthermore, we propose spoon, a statistical framework using Empirical Bayes techniques to remove this bias, leading to more accurate prioritization of SVGs. We demonstrate the performance of spoon in both simulated and real spatial transcriptomics data. A software implementation of our method is available on R/Bioconductor.

The amygdaloid complex mediates learning, memory, and emotions. Understanding the cellular and anatomical features that are specialized in the amygdala of primates versus other vertebrates requires a systematic, anatomically-resolved molecular analysis of constituent cell populations. We analyzed five nuclear subdivisions of the primate amygdala with single-nucleus RNA sequencing in macaques, baboons, and humans to examine gene expression profiles for excitatory and inhibitory neurons and confirmed our results with single-molecule FISH analysis. We identified distinct subtypes of FOXP2 + interneurons in the intercalated cell masses and protein-kinase C-δ interneurons in the central nucleus. We also establish that glutamatergic, pyramidal-like neurons are transcriptionally specialized within the basal, lateral, or accessory basal nuclei. Understanding the molecular heterogeneity of anatomically-resolved amygdalar neuron types provides a cellular framework for improving existing models of how amygdalar neural circuits contribute to cognition and mental health in humans by using nonhuman primates as a translational bridge., Competing Interests: CONFLICT OF INTEREST No conflicts of interest to declare.

Summary: The Human BioMolecular Atlas Program (HuBMAP) constructs the worldwide available platform to research the human body at the cellular level. The HuBMAP Data Portal encompasses a wide range of data resources measured on emerging experimental technologies at a spatial resolution. To broaden access to the HuBMAP Data Portal, we introduce an R client called HuBMAPR available on Bioconductor. This gives an efficient and programmatic interface, enabling researchers to discover and retrieve HuBMAP data easier and faster., Availability: This package is available on GitHub (https://github.com/christinehou11/HuBMAPR) and has been submitted to Bioconductor., Competing Interests: Conflict of interest None declared.

The lateral septum (LS) is a midline, subcortical structure, which regulates social behaviors that are frequently impaired in neurodevelopmental disorders including schizophrenia and autism spectrum disorder. Mouse studies have identified neuronal populations within the LS that express a variety of molecular markers, including vasopressin receptor, oxytocin receptor, and corticotropin releasing hormone receptor, which control specific facets of social behavior. Despite its critical role in regulating social behavior and notable gene expression patterns, comprehensive molecular profiling of the human LS has not been performed. Here, we conducted single nucleus RNA-sequencing (snRNA-seq) to generate the first transcriptomic profiles of the human LS using postmortem human brain tissue samples from 3 neurotypical donors. Our analysis identified 5 transcriptionally distinct neuronal cell types within the human LS that are enriched for TRPC4 , the gene encoding Trp-related protein 4. Differential expression analysis revealed a distinct LS neuronal cell type that is enriched for OPRM1 , the gene encoding the μ-opioid receptor. Leveraging recently generated mouse LS snRNA-seq datasets, we conducted a cross-species analysis. Our results demonstrate that TRPC4 enrichment in the LS is highly conserved between human and mouse, while FREM2, which encodes FRAS1 related extracellular matrix protein 2, is enriched only in the human LS. Together, these results highlight transcriptional heterogeneity of the human LS, and identify robust marker genes for the human LS., Competing Interests: CONFLICT OF INTEREST The authors do not report any conflict of interest.

This proof-of-concept evaluation demonstrates that next-generation sequencing-based liquid biopsy can detect genomic alterations in the blood of cats with cancer and the absence of such alterations in the blood of presumably cancer-free cats. Two cats with cytologically confirmed lymphoma and nine presumably cancer-free cats were included in this analysis. Whole blood was collected from each subject and samples were subjected to DNA extraction, library preparation, and next-generation sequencing. Both cancer-diagnosed subjects had somatic copy number variants (a "cancer signal") identified in cell-free DNA, suggesting the current presence of cancer in these subjects. All nine presumably cancer-free subjects had unremarkable genomic profiles, suggesting the absence of cancer in these subjects. Liquid biopsy using next-generation sequencing of cell-free DNA allows for blood-based detection of cancer-associated genomic alterations in cats. Such technology has the potential to offer considerable utility in veterinary medicine, particularly for the non-invasive prioritization of small cell intestinal lymphoma versus inflammatory bowel disease in cats with gastrointestinal signs. This study lays the foundation for future studies to fully validate this type of testing for use in clinical practice., Competing Interests: CR-P, PN, MM, FM, TT, LM, MK, BF, SG, JR, DG, SH, JT, DT, AF, KK were employed by PetDx. This study received funding from PetDx. The funder had the following involvement with the study: study design, data collection and analysis, decision to publish, and preparation of the manuscript., (Copyright © 2024 Ruiz-Perez, Nakashe, Marshall, Marass, Tang, McLennan, Kroll, Flesner, Gray, Rafalko, Grosu, Hicks, Tynan, Tsui, Flory and Kruglyak.)

Sleep is an essential, tightly regulated biological function. Sleep is also a homeostatic process, with the need to sleep increasing as a function of being awake. Acute sleep deprivation (SD) increases sleep need, and subsequent recovery sleep (RS) discharges it. SD is known to alter brain gene expression in rodents, but it remains unclear which changes are linked to sleep homeostasis, SD-related impairments, or non-sleep-specific effects. To investigate this question, we analyzed RNA-seq data from adult wild-type male mice subjected to 3 and 5-6 hours of SD and 2 and 6 hours of RS after SD. We hypothesized molecular changes associated with sleep homeostasis mirror sleep pressure dynamics as defined by brain electrical activity, peaking at 5-6 hours of SD, and are no longer differentially expressed after 2 hours of RS. We report 5-6 hours of SD produces the largest effect on gene expression, affecting approximately half of the cortical transcriptome, with most differentially expressed genes (DEGs) downregulated. The majority of DEGs normalize after 2 hours of RS and are involved in redox metabolism, chromatin regulation, and DNA damage/repair. Additionally, RS affects gene expression related to mitochondrial metabolism and Wnt-signaling, potentially contributing to its restorative effects. DEGs associated with cholesterol metabolism and stress response do not normalize within 6 hours and may be non-sleep-specific. Finally, DEGs involved in insulin signaling, MAPK signaling, and RNA-binding may mediate the impairing effects of SD. Overall, our results offer insight into the molecular mechanisms underlying sleep homeostasis and the broader effects of SD.

Sleep deprivation (SD) has negative effects on brain and body function. Sleep problems are prevalent in a variety of disorders, including neurodevelopmental and psychiatric conditions. Thus, understanding the molecular consequences of SD is of fundamental importance in biology. In this study, we present the first simultaneous bulk and single-nuclear RNA sequencing characterization of the effects of SD in the male mouse frontal cortex. We show that SD predominantly affects glutamatergic neurons, specifically in layers 4 and 5, and produces isoform switching of over 1500 genes, particularly those involved in splicing and RNA binding. At both the global and cell-type specific level, SD has a large repressive effect on transcription, downregulating thousands of genes and transcripts. As a resource we provide extensive characterizations of cell-types, genes, transcripts, and pathways affected by SD. We also provide publicly available tutorials aimed at allowing readers adapt analyses performed in this study to their own datasets., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Glioblastoma (GBM) is an aggressive brain cancer with limited therapeutic options. Natural killer (NK) cells are innate immune cells with strong anti-tumor activity and may offer a promising treatment strategy for GBM. We compared the anti-GBM activity of NK cells engineered to express interleukin (IL)-15 or IL-21. Using multiple in vivo models, IL-21 NK cells were superior to IL-15 NK cells both in terms of safety and long-term anti-tumor activity, with locoregionally administered IL-15 NK cells proving toxic and ineffective at tumor control. IL-21 NK cells displayed a unique chromatin accessibility signature, with CCAAT/enhancer-binding proteins (C/EBP), especially CEBPD, serving as key transcription factors regulating their enhanced function. Deletion of CEBPD resulted in loss of IL-21 NK cell potency while its overexpression increased NK cell long-term cytotoxicity and metabolic fitness. These results suggest that IL-21, through C/EBP transcription factors, drives epigenetic reprogramming of NK cells, enhancing their anti-tumor efficacy against GBM., Competing Interests: Declaration of interests M.S., M. Daher, R.B., H.R., S.A., Y.L., N.U., E.L., E.J.S., K.R., and The University of Texas MD Anderson Cancer Center have an institutional financial conflict of interest with Takeda Pharmaceutical. R.B., E.J.S., K.R., and The University of Texas MD Anderson Cancer Center have an institutional financial conflict of interest with Affimed GmbH. K.R. participates on the Scientific Advisory Board for Avenge Bio, Virogin Biotech, Navan Technologies, Caribou Biosciences, Bit Bio Limited, Replay Holdings, oNKo Innate, and The Alliance for Cancer Gene Therapy ACGT. K.R. is the Scientific founder of Syena. E.J.S. has served on the Scientific Advisory Board for Adaptimmune, Axio, Celaid, FibroBiologics, Navan Technologies, New York Blood Center and Novartis. M Daher participates on the Scientific Advisory Board for Cellsbin. M.S., H.S., E.J.S., and K.R. have filed for a patent (20230074303); “Cell immunotherapy for the treatment of cancer.”, (Copyright © 2024 Elsevier Inc. All rights reserved.)

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future., Competing Interests: Conflict of Interest: The authors have no declared conflicts of interests.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools., (© 2024. Crown.)

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias., (© The Author(s) 2023. Published by Oxford University Press.)

Quality control (QC) is a crucial step to ensure the reliability and accuracy of the data obtained from RNA sequencing experiments, including spatially-resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-nucleus RNA sequencing (snRNA-seq) methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts unique to SRT. Here, we introduce SpotSweeper, spatially-aware QC methods for identifying local outliers and regional artifacts in SRT. SpotSweeper evaluates the quality of individual spots relative to their local neighborhood, thus minimizing bias due to biological heterogeneity, and uses multiscale methods to detect regional artifacts. Using SpotSweeper on publicly available data, we identified a consistent set of Visium barcodes/spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts, resulting in improved downstream clustering and marker gene detection for spatial domains., Competing Interests: Conflict of Interest None declared.

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and developmental stages, contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function and underpin disease pathogenesis. Analyzing DTU via RNA sequencing (RNA-seq) data is vital, but the genetic heterogeneity in populations with complex diseases presents an intricate challenge due to diverse causal events and undetermined subtypes. Although the majority of common diseases in humans are categorized as complex, state-of-the-art DTU analysis methods often overlook this heterogeneity in their models. We therefore developed SPIT, a statistical tool that identifies predominant subgroups in transcript usage within a population along with their distinctive sets of DTU events. This study provides comprehensive assessments of SPIT's methodology and applies it to analyze brain samples from individuals with schizophrenia, revealing previously unreported DTU events in six candidate genes., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To validate the performance of a novel, integrated test for canine cancer screening that combines cell-free DNA quantification with next-generation sequencing (NGS) analysis., Sample: Retrospective data from a total of 1,947 cancer-diagnosed and presumably cancer-free dogs were used to validate test performance for the detection of 7 predefined cancer types (lymphoma, hemangiosarcoma, osteosarcoma, leukemia, histiocytic sarcoma, primary lung tumors, and urothelial carcinoma), using independent training and testing sets., Methods: Cell-free DNA quantification data from all samples were analyzed using a proprietary machine learning algorithm to determine a Cancer Probability Index (High, Moderate, or Low). High and Low Probability of Cancer were final result classifications. Moderate cases were additionally analyzed by NGS to arrive at a final classification of High Probability of Cancer (Cancer Signal Detected) or Low Probability of Cancer (Cancer Signal Not Detected)., Results: Of the 595 dogs in the testing set, 89% (n = 530) received a High or Low Probability result based on the machine learning algorithm; 11% (65) were Moderate Probability, and NGS results were used to assign a final classification. Overall, 87 of 122 dogs with the 7 predefined cancer types were classified as High Probability and 467 of 473 presumably cancer-free dogs were classified as Low Probability, corresponding to a sensitivity of 71.3% for the predefined cancer types at a specificity of 98.7%., Clinical Relevance: This integrated test offers a novel option to screen for cancer types that may be difficult to detect by physical examination at a dog's wellness visit.

Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer's and Parkinson's disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats., Competing Interests: LW, HD, SK, AS, KM, MT, RB, JK, SP, TH, LC, KM, KM, SH No competing interests declared, MT The authors declare that they have no competing interests. Matthew N. Tran (MNT) is now a full-time employee at 23andMe and whose current work is unrelated to the contents of this manuscript. His contributions to this manuscript were made while previously employed at the Lieber Institute for Brain Development (LIBD), (© 2023, Weber, Divecha et al.)

Objective: The purpose of this study was to evaluate the performance of a next-generation sequencing-based liquid biopsy test for cancer monitoring in dogs., Samples: Pre- and postoperative blood samples were collected from dogs with confirmed cancer diagnoses originally enrolled in the CANcer Detection in Dogs (CANDiD) study. A subset of dogs also had longitudinal blood samples collected for recurrence monitoring., Methods: All cancer-diagnosed patients had a preoperative blood sample in which a cancer signal was detected and had at least 1 postoperative sample collected. Clinical data were used to assign a clinical disease status for each follow-up visit., Results: Following excisional surgery, in the absence of clinical residual disease at the postoperative visit, patients with Cancer Signal Detected results at that visit were 1.94 times as likely (95% CI, 1.21 to 3.12; P = .013) to have clinical recurrence within 6 months compared to patients with Cancer Signal Not Detected results. In the subset of patients with longitudinal liquid biopsy samples that had clinical recurrence documented during the study period, 82% (9/11; 95% CI, 48% to 97%) had Cancer Signal Detected in blood prior to or concomitant with clinical recurrence; in the 6 patients where molecular recurrence was detected prior to clinical recurrence, the median lead time was 168 days (range, 47 to 238)., Clinical Relevance: Next-generation sequencing-based liquid biopsy is a noninvasive tool that may offer utility as an adjunct to current standard-of-care clinical assessment for cancer monitoring; further studies are needed to confirm diagnostic accuracy in a larger population.

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the "scale-agnostic" $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package., (© The Author 2022. Published by Oxford University Press.)

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods., (© 2023. The Author(s).)

Summary: The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows., Availability and Implementation: The open source R package escheR is freely available on Bioconductor (https://bioconductor.org/packages/escheR)., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press.)

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/., Competing Interests: A.E.J. is now a full-time employee at Neumora Therapeutics, a for-profit biotechnology company, which is unrelated to the contents of this manuscript. J.L.C. is now a full-time employee at Delfi Diagnostics, a for-profit biotechnology company, which is unrelated to the contents of this manuscript. Their contributions to the manuscript were made while previously employed by the Lieber Institute for Brain Development. All other authors declare no competing interests., (© The Author(s) 2023.)

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes., (© 2023. The Author(s).)

Background: Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay., Results: We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method., Conclusions: Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity., (© 2023. BioMed Central Ltd., part of Springer Nature.)

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which couples spatial gene expression with immunofluorescence protein co-detection, to evaluate its ability to quantify changes in spatial gene expression with respect to amyloid-β (Aβ) and hyperphosphorylated tau (pTau) pathology in post-mortem human brain tissue from individuals with Alzheimer's disease (AD). We identified transcriptomic signatures associated with proximity to Aβ in the human inferior temporal cortex (ITC) during late-stage AD, which we further investigated at cellular resolution with combined immunofluorescence and single molecule fluorescent in situ hybridization (smFISH). The study provides a data analysis workflow for Visium-SPG, and the data represent a proof-of-principal for the power of multi-omic profiling in identifying changes in molecular dynamics that are spatially-associated with pathology in the human brain. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes at https://research.libd.org/Visium_SPG_AD/., Competing Interests: Author disclosure statement SRW and MM are employees of 10x Genomics. All other authors declare no conflicts of interest.

Activity-regulated gene (ARG) expression patterns in the hippocampus (HPC) regulate synaptic plasticity, learning, and memory, and are linked to both risk and treatment responses for many neuropsychiatric disorders. The HPC contains discrete classes of neurons with specialized functions, but cell type-specific activity-regulated transcriptional programs are not well characterized. Here, we used single-nucleus RNA-sequencing (snRNA-seq) in a mouse model of acute electroconvulsive seizures (ECS) to identify cell type-specific molecular signatures associated with induced activity in HPC neurons. We used unsupervised clustering and a priori marker genes to computationally annotate 15,990 high-quality HPC neuronal nuclei from N = 4 mice across all major HPC subregions and neuron types. Activity-induced transcriptomic responses were divergent across neuron populations, with dentate granule cells being particularly responsive to activity. Differential expression analysis identified both upregulated and downregulated cell type-specific gene sets in neurons following ECS. Within these gene sets, we identified enrichment of pathways associated with varying biological processes such as synapse organization, cellular signaling, and transcriptional regulation. Finally, we used matrix factorization to reveal continuous gene expression patterns differentially associated with cell type, ECS, and biological processes. This work provides a rich resource for interrogating activity-regulated transcriptional responses in HPC neurons at single-nuclei resolution in the context of ECS, which can provide biological insight into the roles of defined neuronal subtypes in HPC function., (© 2023 The Authors. Hippocampus published by Wiley Periodicals LLC.)

High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com., Competing Interests: The authors declare that they have no competing interests., (© The Author(s) 2023.)

Age-related somatic genomic alterations in hematopoietic cell lines have been well characterized in humans; however, this phenomenon has not been well studied in other species. Next-generation sequencing-based liquid biopsy testing for cancer detection was recently developed for dogs and has been used to study the genomic profiles of blood samples from thousands of canine patients since 2021. In this study, 4870 client-owned dogs with and without a diagnosis or suspicion of cancer underwent liquid biopsy testing by this method. Copy number variants detected exclusively in genomic DNA derived from white blood cells (WBC gDNA-specific CNVs) were observed in 126 dogs (2.6%; 95% CI: 2.2-3.1); these copy number variants were absent from matched plasma cell-free DNA, and from tumor tissue in dogs with concurrent cancer. These findings were more common in older dogs and were persistent in WBC gDNA in over 70% of patients, with little to no change in the amplitude of the signal across longitudinal samples. Many of these alterations were observed at recurrent locations in the genome across subjects; the most common finding was a partial loss on CFA25, typically accompanied by a partial gain on the same chromosome. These early findings suggest that age-related somatic alterations may be present at an appreciable frequency in the general canine population. Further research is needed to determine the clinical significance of these findings.

Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG ., (© 2023. The Author(s).)

Objective: To review ordering patterns, positivity rates, and outcome data for a subset of consecutive samples submitted for a commercially available, blood-based multicancer early-detection liquid biopsy test for dogs using next-generation sequencing at 1 laboratory., Sample: 1,500 consecutively submitted blood samples from client-owned dogs with and without clinical suspicion and/or history of cancer for prospective liquid biopsy testing between December 28, 2021, and June 28, 2022., Procedures: We performed a retrospective observational study, reviewing data from 1,500 consecutive clinical samples submitted for liquid biopsy testing. Outcome data were obtained via medical record review, direct communication with the referring clinic, and/or a patient outcome survey through October 16, 2022., Results: Sixty-four percent (910/1,419) of reportable samples were submitted for cancer screening, 26% (366/1,419) for aid in diagnosis, and 10% (143/1,419) for other indications. The positivity rate was 25.4% (93/366) in aid-in-diagnosis patients and 4.5% (41/910) in screening patients. Outcome data were available for 33% (465/1,401) of patients, and outcomes were classifiable for 428 patients. The relative observed sensitivity was 61.5% (67/109) and specificity was 97.5% (311/319). The positive predictive value was 75.0% (21/28) for screening patients and 97.7% (43/44) for aid-in-diagnosis patients, and the time to diagnostic resolution following a positive result was < 2 weeks in most cases., Clinical Relevance: Liquid biopsy using next-generation sequencing represents a novel tool for noninvasive detection of cancer in dogs. Real-world clinical performance meets or exceeds expectations established in the test's clinical validation study.

COVID-19 is associated with serious cardiovascular complications, with incompletely understood mechanism(s). Pericytes have key functions in supporting endothelial cells and maintaining vascular integrity. We demonstrate that human cardiac pericytes are permissive to SARS-CoV-2 infection in organotypic slice and primary cell cultures. Viral entry into pericytes is mediated by endosomal proteases, and infection leads to up-regulation of inflammatory markers, vasoactive mediators, and nuclear factor kappa-B-dependent cell death. Furthermore, we present evidence of cardiac pericyte infection in COVID-19 myocarditis patients. These data demonstrate that human cardiac pericytes are susceptible to SARS-CoV-2 infection and suggest a role for pericyte infection in COVID-19., Competing Interests: This research was supported by National Institutes of Health/National Center for Advancing Translational Sciences grant UH3 HL141800-04S1 COVID-19 supplement for an emergency response to COVID-19 (to Dr Rentschler), National Institutes of Health National Heart, Lung, and Blood Institute/National Center for Advancing Translational Sciences UH3 HL 141800 (to Dr Rentschler), and the National Science Foundation GRFP grant DGE-1745038 (to Dr Brumback). The Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant# UL1TR002345 from the National Center for Research Resources, a component of the National Institutes of Health, and National Institutes of Health Roadmap for Medical Research. Dr Diamond is supported by the National Institutes of Health (R01 AI157155); is a consultant for Inbios, Vir Biotechnology, Moderna, Immunome, and Senda Biosciences; and has received unrelated sponsored research agreement funding from Moderna, Vir Biotechnology, and Emergent BioSolutions. Dr Lavine is supported by the National Institutes of Health (R01 HL138466, R01 HL139714, R01 HL151078), Leducq Foundation Network (#20CVD02), Burroughs Wellcome Fund (1014782), Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital (CH-II-2015-462, CH-II-2017-628, PM-LI-2019-829), and Foundation of Barnes-Jewish Hospital (8038-88); is a consultant for Implicit Biosciences and Flame Biosciences; is a member of the Medtronic: DT-PAS/APOGEE trial advisory board; and has received funding and unrelated sponsored research agreements from Amgen and Novartis. Dr Rentschler is supported by the National Institutes of Health (R01 HL130212, UH3 HL141800), Burroughs Wellcome Fund (1009884), Foundation of Barnes-Jewish Hospital, and Additional Ventures Cures Collaborative. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2023 The Authors.)

Glycogen synthase kinase 3 (GSK-3) inhibition has emerged as a potential therapeutic target for several diseases, including cancer. However, the role for GSK-3 regulation of human cardiac electrophysiology remains ill-defined. We demonstrate that SB216763, a GSK-3 inhibitor, can acutely reduce conduction velocity in human cardiac slices. Combined computational modeling and experimental approaches provided mechanistic insight into GSK-3 inhibition-mediated changes, revealing that decreased sodium-channel conductance and tissue conductivity may underlie the observed phenotypes. Our study demonstrates that GSK-3 inhibition in human myocardium alters electrophysiology and may predispose to an arrhythmogenic substrate; therefore, monitoring for adverse arrhythmogenic events could be considered., Competing Interests: This study has received grants from the National Science Foundation (NSF) and the National Institutes of Health (NIH): NSF GRFP DGE-1745038 and NIH T32 HL125241-03 to Ms Brumback, NIH T32HL007081 to Dr Jimenez, NIH T32HL134635 to Mr Zhang, and NIH R01 HL 130212 and UH3 HL 141800 to Dr Rentschler. Dr Rentschler holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2022 The Authors.)

The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse., (© 2022 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary., Results: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application., Conclusions: spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform., (© 2022. The Author(s).)

Summary: SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages., Availability and Implementation: The SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Cancer is the leading cause of death in dogs, yet there are no established screening paradigms for early detection. Liquid biopsy methods that interrogate cancer-derived genomic alterations in cell-free DNA in blood are being adopted for multi-cancer early detection in human medicine and are now available for veterinary use. The CANcer Detection in Dogs (CANDiD) study is an international, multi-center clinical study designed to validate the performance of a novel multi-cancer early detection "liquid biopsy" test developed for noninvasive detection and characterization of cancer in dogs using next-generation sequencing (NGS) of blood-derived DNA; study results are reported here. In total, 1,358 cancer-diagnosed and presumably cancer-free dogs were enrolled in the study, representing the range of breeds, weights, ages, and cancer types seen in routine clinical practice; 1,100 subjects met inclusion criteria for analysis and were used in the validation of the test. Overall, the liquid biopsy test demonstrated a 54.7% (95% CI: 49.3-60.0%) sensitivity and a 98.5% (95% CI: 97.0-99.3%) specificity. For three of the most aggressive canine cancers (lymphoma, hemangiosarcoma, osteosarcoma), the detection rate was 85.4% (95% CI: 78.4-90.9%); and for eight of the most common canine cancers (lymphoma, hemangiosarcoma, osteosarcoma, soft tissue sarcoma, mast cell tumor, mammary gland carcinoma, anal sac adenocarcinoma, malignant melanoma), the detection rate was 61.9% (95% CI: 55.3-68.1%). The test detected cancer signal in patients representing 30 distinct cancer types and provided a Cancer Signal Origin prediction for a subset of patients with hematological malignancies. Furthermore, the test accurately detected cancer signal in four presumably cancer-free subjects before the onset of clinical signs, further supporting the utility of liquid biopsy as an early detection test. Taken together, these findings demonstrate that NGS-based liquid biopsy can offer a novel option for noninvasive multi-cancer detection in dogs., Competing Interests: AF, KMK, JAT, LMM, JMR, PCF, GEH, FM, PN, CAR-P, DMF, TJ, RM-P, KW, ALM-W, DSG, AP, KM, SCH, KML, LH, JC, IC, and DWYT are employed by or affiliated with PetDx; and receive compensation from PetDx and/or hold vested or unvested equity in PetDx. Drs. Flesner and Leibman are members of the PetDx clinical advisory board and receive compensation and equity. Dr. Borgatti has ownership interest (including patents) in a patent entitled “Reduction of EGFR therapeutic toxicity” filed by the University of Minnesota Office of Technology Commercialization. Drs. Cahalane, Phillips, and Aiken are investors in PetDx. Drs. Jensen, van den Boom, Dharajiya, and Diaz are advisors for PetDx and hold vested or unvested equity in PetDx. Drs. Chorny, Kruglyak, Grosu, Marass, Ruiz-Perez and Tsui hold pending patent applications related to technology described in this work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction., Competing Interests: The authors have declared that no competing interests exist.

Single-cell gene expression technologies are powerful tools to study cell types in the human brain, but efforts have largely focused on cortical brain regions. We therefore created a single-nucleus RNA-sequencing resource of 70,615 high-quality nuclei to generate a molecular taxonomy of cell types across five human brain regions that serve as key nodes of the human brain reward circuitry: nucleus accumbens, amygdala, subgenual anterior cingulate cortex, hippocampus, and dorsolateral prefrontal cortex. We first identified novel subpopulations of interneurons and medium spiny neurons (MSNs) in the nucleus accumbens and further characterized robust GABAergic inhibitory cell populations in the amygdala. Joint analyses across the 107 reported cell classes revealed cell-type substructure and unique patterns of transcriptomic dynamics. We identified discrete subpopulations of D1- and D2-expressing MSNs in the nucleus accumbens to which we mapped cell-type-specific enrichment for genetic risk associated with both psychiatric disease and addiction., Competing Interests: Declaration of interests A.E.J. is employed by a for-profit biotechnology startup company (company name pending), which is unrelated to the content of this manuscript. The remaining authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, to our knowledge these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the signal in natural genetic variation., Results: Here, we performed in silico benchmark evaluations by combining raw sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden. Our results confirm that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design, although high proportions of ambient RNA from cell debris reduce performance., Conclusions: This strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our in silico benchmark evaluations, available at https://github.com/lmweber/snp-dmx-cancer., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)