Biology subjects

Osorio, D.

Publications and source records attributed to Osorio, D..

At least 19 recordsLinked to original sources

bioRxiv2025

Pan-cancer analysis of patient-specific gene regulatory landscapes identifies recurrent PD-1 pathway dysregulation linked to outcome

Cancer heterogeneity remains a major challenge to the development of broadly effective therapies. Although gene expression-based stratification has advanced our understanding of molecular subtypes, it often overlooks the regulatory mechanisms driving tumor progression and therapeutic resistance. These mechanisms are governed by gene regulatory networks--complex systems of transcription factors and other regulators that control gene expression. To investigate regulatory heterogeneity at the patient level, we inferred individual gene regulatory networks for over 9,000 tumors across 33 cancer types from The Cancer Genome Atlas enabling a comprehensive pancancer analysis of inter-patient regulatory variation. Our analysis uncovered novel regulatory subtypes and revealed recurrent dysregulation of the PD-1 signaling pathway in 23 cancer types. This dysregulation was associated with patient outcomes in 12 cancers and appeared largely independent of established molecular subtypes and immune cell composition, suggesting alternative regulatory mechanisms. We identified both well-characterized (FOS, JUNB, ATF3) and previously unrecognized (RFX7, ELF1, NFYA, NFYC) transcriptional regulators of PD-L1 (CD274), highlighting new candidate targets for modulating immune checkpoint activity across tumor types. Our work demonstrates the power of single-sample gene regulatory networks to uncover hidden layers of tumor biology, improve patient stratification, and inform future research in immunotherapy.

bioinformatics↗

bioRxiv2025

Image-based leaf SPAD value and chlorophyll measurement using a mobile phone: enabling accessible and sustainable crop management

AO_SCPLOWBSTRACTC_SCPLOWThis study evaluates a practical, low-cost solution for image-based leaf SPAD (Soil and Plant Analysis Development) value and chlorophyll content monitoring using a mobile phone. We compare laboratory assay and SPAD-502+ measurements with image-based estimates from a mobile phone app (PhotoFolia). Performance is tested for four commercial rice varieties grown in Thailand. Results show that the image-based method can predict SPAD values within {+/-} 1.2 units Mean Absolute Error (MAE) and- chlorophyll concentrations within 7.2% Mean Absolute Percentage Error (MAPE) of laboratory results. Achieving a SPAD value error close to the industry standard of {+/-}1 unit and a relative error of less than 10% in chlorophyll concentration estimation (compared to a laboratory method) demonstrates that an image-based approach using standard mobile phones can serve as an accessible, low-cost tool for on-farm chlorophyll monitoring, without the need for specialised equipment. Key Points / HighlightsNovel low-cost approach for chlorophyll assay and SPAD-value measurement using standard mobile phone. Achieves accuracy comparable to commercial tools. Eliminates need for specialised sensors or laboratory equipment. ImpactThis study demonstrates that mobile phone-based image analysis can accurately estimate leaf SPAD and chlorophyll levels in rice under ambient lighting conditions, offering a low-cost, accessible tool for monitoring plant health.

plant biology↗

bioRxiv2023

Spontaneous biases enhance generalisation in the neonate brain

The ability to use sparse evidence to produce adaptive responses in new contexts and to new stimuli (inductive generalisation) is central to biological and artificial intelligence. Young and inexperienced animals require very little evidence to generalise, raising the question of whether the neonate brain is evolutionarily prepared (predisposed) for generalisation. To understand the principles of spontaneous generalisation, we exposed neonate chicks to an artificial social partner of a specific colour, and measured generalisation by comparing responses to novel and familiar stimuli along either the red-yellow or the blue-green colour continuum. Generalisation responses were inconsistent with an unbiased model, showing biases such as asymmetrical generalisation gradients, faster learning for particular colours (red and blue over yellow and green), preferences for unfamiliar stimuli and different time courses in learning. Moreover, the chicks generalisation behaviour was consistent with a Bayesian theoretical model that explicitly incorporates predispositions as initial preferences and treats the learning process as an update of spontaneous preferences. These results show that neonate animals are evolutionarily prepared for generalisation, via biases that do not depend on experience, reinforcement or supervision. Biases that facilitate generalisation are tuned to distinctive features that are unusual in the natural environment, such as the red and blue colours. Predispositions facilitate or hinder learning in the inexperienced brain, determining how experience is used to update the likelihood of predictive models. Neonate animals use spontaneous biases to solve the problem of induction.

neuroscience↗

bioRxiv2023

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. We present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single cell/nuclei RNA-seq data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperforms 12 other gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPIONs scalability to population-level analyses using a single-cell RNA-seq atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences detected by SCORPION between tumor regions are consistent across population cohorts, as well as with our understanding of disease progression and elucidate phenotypic regulators that may impact patient survival.

bioinformatics↗

bioRxiv2022

Drug combination prioritization for cancer treatment using single-cell RNA-seq based transfer learning

Precision oncology seeks to match patients to the optimal pharmacological regimen; yet, due to tumor heterogeneity, this is challenging. Numerous studies have been conducted to produce clinically relevant pharmacological response forecasts by integrating modern machine learning algorithms and several data types. Insufficient patient numbers and lack of knowledge of the molecular targets for each drug under study limit their use. As a proof of concept, we use single-cell RNA-seq based transfer learning to contextualize patients tumor cells in terms of their more similar cell lines with known susceptibility to drug combinations. Our objective is to maximize the translational potential of in-vitro assays for identifying synergistic drug combinations and prioritizing them for clinical use. Consistent findings in a cohort of breast cancer patients corroborated our understanding of the diseases molecular subtypes. To aid in creating personalized treatments and data-driven clinical trials, we identified the most prevalent cell lines and prioritized synergistic combinations based on tumor compositions at various resolution levels.

bioinformatics↗

bioRxiv2022

Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

Developing novel cancer treatments is a challenging task that can benefit from computational techniques matching transcriptional signatures to large-scale drug response data. Here, we present retriever, a tool that extracts robust disease-specific transcrip-tional drug response profiles based on cellular response profiles to hundreds of compounds from the LINCS-L1000 project. We used retriever to extract transcriptional drug response signatures of triple-negative breast cancer (TNBC) cell lines and combined these with a single-cell RNA-seq breast cancer atlas to predict drug combinations that antagonize TNBC-specific disease signatures. After systematically testing 152 drug response profiles and 11,476 drug combinations, we identified the combination of kinase inhibitors QL-XII-47 and GSK-690693 as the topmost promising candidate for TNBC treatment. Our new computational approach allows the identification of drugs and drug combinations targeting specific tumor cell types and subpopulations in individual patients. It is, therefore, highly suitable for the development of new personalized cancer treatment strategies.

bioinformatics↗

bioRxiv2022

A model of colour appearance based on efficient coding of natural images

An objects colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and "illusions" have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an objects perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial bands lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the models ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

neuroscience↗

bioRxiv2021

rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

MotivationCharacterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. ResultsHere, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and ImplementationrPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB. Contactdaniecos@uio.no Supplementary informationCode to replicate the case example and figure 1 is available at https://github.com/dosorio/rPanglaoDB O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/446161v1_fig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@b4e67forg.highwire.dtl.DTLVardef@88a46aorg.highwire.dtl.DTLVardef@e25681org.highwire.dtl.DTLVardef@19d5bb7_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Characterization of the fibrocytes transcriptome. (A) Identification of cells expressing marker genes that differentiate fibrocytes identity from macrophages and fibroblasts (CD34, ACTA2, FN1, Collagen V, FAP and SIRPA). (B) Cross validation of the identified cells expressing CD34+, ACTA2+, COL5A1+, COL5A2+, COL5A3+, FN1+, FAP+, SIRPA+, PTPRC+, MME+, and SEMA7A+ by kernel density estimation through the Nebulosa package. (C) Volcano plot displaying the differential expression between fibrocytes and the fibroblasts collected in the same merged samples. (D) Enrichment of the prostaglandin biosynthesis and regulation pathway using GSEA through the fgsea package. (E) Enrichment of the prostaglandin biosynthesis and regulation pathway using ssGSEA through the GSVA package. C_FIG

bioinformatics↗

bioRxiv2021

scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks

Bigger PictureO_LIGene knockout (KO) experiments, using genetically altered animals, are a proven powerful approach to elucidate the role of a gene in a biological process. However, systematic KO experiments targeting many genes are usually prohibitive due to limited experimental and animal resources. Here, we present scTenifoldKnk, an efficient virtual KO tool that allows the systematic deletion of many genes individually. scTenifoldKnk uses single-cell RNA sequencing (scRNAseq) data from wild-type (WT) samples to predict gene function in a cell type-specific manner. We show that predictions made by scTenifoldKnk recapitulate findings from real-animal KO experiments. scTenifoldKnk has proven to be a powerful and effective approach for elucidating gene function, prioritizing KO targets, predicting experimental outcomes before real-animal KO experiments are conducted. C_LI HighlightsO_LIscTenifoldKnk performs virtual KO experiments using scRNAseq data. C_LIO_LIscTenifoldKnk only requires data from WT samples; no data is needed from KO samples. C_LIO_LIPredictions made by scTenifoldKnk recapitulate findings from real-animal KO experiments. C_LI Data Science Maturity Level O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=14 SRC="FIGDIR/small/436484v2_ufig1.gif" ALT="Figure 1"> View larger version (8K): org.highwire.dtl.DTLVardef@157126aorg.highwire.dtl.DTLVardef@17a2e76org.highwire.dtl.DTLVardef@805c4dorg.highwire.dtl.DTLVardef@12bdc1b_HPS_FORMAT_FIGEXP M_FIG C_FIG eTOC blurbscTenifoldKnk is a machine learning workflow performing virtual KO experiments to predict gene function. It constructs gene regulatory networks using single-cell RNA sequencing data from wild-type samples and then computationally deletes target genes. Real-data applications demonstrate that scTenifoldKnk recapitulates findings of real-animal KO experiments and accurately predicts gene function in analyzed cells. SummaryGene knockout (KO) experiments are a proven, powerful approach for studying gene function. However, systematic KO experiments targeting a large number of genes are usually prohibitive due to the limit of experimental and animal resources. Here, we present scTenifoldKnk, an efficient virtual KO tool that enables systematic KO investigation of gene function using data from single-cell RNA sequencing (scRNAseq). In scTenifoldKnk analysis, a gene regulatory network (GRN) is first constructed from scRNAseq data of wild-type samples, and a target gene is then virtually deleted from the constructed GRN. Manifold alignment is used to align the resulting reduced GRN to the original GRN to identify differentially regulated genes, which are used to infer target gene functions in analyzed cells. We demonstrate that the scTenifoldKnk-based virtual KO analysis recapitulates the main findings of real-animal KO experiments and recovers the expected functions of genes in relevant cell types.

bioinformatics↗

bioRxiv2020

Colourfulness as a possible measure of object proximity in the larval zebrafish brain

The encoding of light increments and decrements by separate On- and Off- systems is a fundamental ingredient of vision, which supports the detection of edges in space and time and makes efficient use of limited dynamic range of visual neurons [1]. Theory predicts that the neural representation of On- and Off-signals should be approximately balanced, including across an animals full visible spectrum. Here we find that larval zebrafish violate this textbook expectation: in the fish brain, UV-stimulation near exclusively gives On-responses, blue/green-stimulation mostly Off- responses, and red-light alone elicits approximately balanced On- and Off-responses (see also [2-4]). We link these findings to zebrafish visual ecology, and suggest that the observed spectral tuning boosts the encoding of object "colourfulness", which correlates with object proximity in their underwater world [5].

neuroscience↗

bioRxiv2020

Src-dependent NM2A tyrosine-phosphorylation regulates actomyosin dynamics

Non-muscle myosin 2A (NM2A) is a key cytoskeletal enzyme that along with actin assembles into actomyosin filaments inside cells. NM2A is fundamental in cellular processes requiring force generation such as cell adhesion, motility and cell division, and plays important functions in different stages of development and during the progression of viral and bacterial infections. We previously identified a novel tyrosine phosphorylation on residue 158 (pTyr158) in the motor domain of NM2A. This phosphorylation is dependent on Src kinase and is promoted by Listeria monocytogenes infection of epithelial cells, however its role is unknown. Here we show that Listeriolysin O (LLO), the pore-forming toxin (PFT) secreted by L. monocytogenes, is sufficient to trigger NM2A pTyr158 by activating Src, an upstream regulator of actomyosin remodeling. We further address the role of NM2A pTyr158 on the organization and dynamics of the actomyosin cytoskeleton and find that, by controlling the activation of the NM2A, the status of the pTyr158 alters cytoskeletal organization, dynamics of focal adhesions and cell motility. In vitro, we observe that non-phosphorylatable and phospho-mimetic versions of NM2A at Tyr158 display motor and ATPase activities similar to the wild-type NM2A, which indicates that the phenotype of these mutants in cells is independent of their ability to translocate actin filaments. Importantly, we find the regulation of this phosphorylation site to be of physiological relevance in Caenorhabditis elegans, in particular in response to intoxication by a PFT and to heat shock. We conclude that the control of the phosphorylation status at NM2A Tyr158 is a conserved trait that contributes to the regulation of actomyosin dynamics and the ability of cells to respond to bacterial infection. We propose Src-dependent NM2A pTyr158 as a novel layer of regulation of the actomyosin cytoskeleton.

cell biology↗

bioRxiv2020

PIG-1 MELK-dependent phosphorylation of nonmuscle myosin II promotes apoptosis through CES-1 Snail partitioning

The mechanism(s) through which mammalian kinase MELK promotes tumorigenesis is not understood. We find that the C. elegans orthologue of MELK, PIG-1, promotes apoptosis by partitioning an anti-apoptotic factor. The C. elegans NSM neuroblast divides to produce a larger cell that differentiates into a neuron and a smaller cell that dies. We find that in this context, PIG-1 is required for partitioning of CES-1 Snail, a transcriptional repressor of the pro-apoptotic gene egl-1 BH3-only. pig-1 MELK is controlled by both a ces-1 Snail- and par-4 LKB1-dependent pathway, and may act through phosphorylation and cortical enrichment of nonmuscle myosin II prior to neuroblast division. We propose that pig-1 MELK-induced local contractility of the actomyosin network plays a conserved role in the acquisition of the apoptotic fate. Our work also uncovers an auto-regulatory loop through which ces-1 Snail controls its own activity through the formation of a gradient of CES-1 Snail protein. Significance StatementApoptosis is critical for the elimination of unwanted cells. What distinguishes wanted from unwanted cells in developing animals is poorly understood. We report that in the C. elegans NSM neuroblast lineage, the level of CES-1, a Snail-family member and transcriptional repressor of the pro-apoptotic gene egl-1, contributes to this process. In addition, we demonstrate that C. elegans PIG-1, the orthologue of mammalian proto-oncoprotein MELK, plays a critical role in controlling CES-1Snail levels. Specifically, during NSM neuroblast division, PIG-1MELK controls partitioning of CES-1Snail into one but not the other daughter cell thereby promoting the making of one wanted and one unwanted cell. Furthermore, we present evidence that PIG-1MELK acts prior to NSM neuroblast division by locally activating the actomyosin network.

developmental biology↗

bioRxiv2020

Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA sequencing data quality control

MotivationQuality control (QC) is a critical step in single-cell RNA-seq (scRNA-seq) data analysis. Low-quality cells are removed from the analysis during the QC process to avoid misinterpretation of the data. One of the important QC metrics is the mitochondrial proportion (mtDNA%), which is used as a threshold to filter out low-quality cells. Early publications in the field established a threshold of 5% and since then, it has been used as a default in several software packages for scRNA-seq data analysis and adopted as a standard in many scRNA-seq studies. However, the validity of using a uniform threshold across different species, single-cell technologies, tissues, and cell types has not been adequately assessed. ResultsWe systematically analyzed 5,530,106 cells reported in 1,349 annotated datasets available in the PanglaoDB database and found that the average mtDNA% in scRNA-seq data across human tissues is significantly higher than in mouse tissues. This difference is not confounded by the platform used to generate the data. Based on this finding, we propose new reference values of the mtDNA% for 121 tissues of mice and 44 tissues of humans. In general, for mouse tissues, the 5% threshold performs well to distinguish between healthy and low-quality cells. However, for human tissues, the 5% threshold should be reconsidered as it fails to accurately discriminate between healthy and low-quality cells in 29.5% (13 of 44) tissues analyzed. We conclude that omitting the mtDNA% QC filter or adopting a suboptimal mtDNA% threshold may lead to erroneous biological interpretations of scRNA-seq data. AvailabilityThe code used to download datasets, perform the analyzes, and produce the figures is available at https://github.com/dosorio/mtProportion Contactdcosorioh@tamu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data

Constructing and comparing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNAseq) data has the potential to reveal critical components in the underlying regulatory networks regulating different cellular transcriptional activities. Here, we present a robust and powerful machine learning workflow--scTenifoldNet--for comparative GRN analysis of single cells. The scTenifoldNet workflow, consisting of principal component regression, low-rank tensor approximation, and manifold alignment, constructs and compares transcriptome-wide single-cell GRNs (scGRNs) from different samples to identify gene expression signatures shifting with cellular activity changes such as those associated with pathophysiological processes and responses to environmental perturbations. We used simulated data to benchmark scTenifoldNets performance, and then applied scTenifoldNet to several real data sets. In real-data applications, scTenifoldNet identified highly specific changes in gene regulation in response to acute morphine treatment, an antibody anticancer drug, gene knockout, double-stranded RNA stimulus, and amyloid-beta plaques in various types of mouse and human cells. We anticipate that scTenifoldNet can help achieve breakthroughs through constructing and comparing scGRNs in poorly characterized biological systems, by deciphering the full cellular and molecular complexity of the data. HighlightsO_LIscTenifoldNet is a machine learning workflow built upon principal component regression, low-rank tensor approximation, and manifold alignment C_LIO_LIscTenifoldNet uses single-cell RNA sequencing (scRNAseq) data to construct single-cell gene regulatory networks (scGRNs) C_LIO_LIscTenifoldNet compares scGRNs of different samples to identify differentially regulated genes C_LIO_LIReal-data applications demonstrate that scTenifoldNet accurately detects specific signatures of gene expression relevant to the cellular systems tested. C_LI Short abstractWe present scTenifoldNet--a machine learning workflow built upon principal component regression, low-rank tensor approximation, and manifold alignment--for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing (scRNAseq). scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.

bioinformatics↗

bioRxiv2019

Object Colours, Material Properties and Animal Signals

Colour is commonly regarded as an absolute measure of object properties, but most work on visual communication signals is concerned with colour differences, typically scaled by just noticeable differences (JNDs). Object colour solids represent the colour gamut of reflective materials for an eye. The geometry of colour solids reveals general relationships between colours and object properties which can explain why certain colours are significant to animals and evolve as signals. We define a measure of colour vividness, such that points on the surface are maximally vivid and the grey centre is minimally vivid. We show that a vivid colour for one animal is likely to vivid for others, and highly vivid colours are less easily mimicked than less vivid colours. Further, vivid colours such as black, white, red, blue and light, unsaturated shades are produced pure or orderly materials. This kind of material needs to created and maintained against entropic processes that would otherwise degrade or destroy them. Vivid coloration is therefore indicative of ecological affordance or biological function, so that it is valuable to have attentional biases towards these colours regardless of any specific significance.

animal behavior and cognition↗

bioRxiv2019

Single cell expression variability in highly homogeneous populations of human cells is widespread, heritable, and implies cell function

Because of recent technological developments, single-cell assays such as single-cell RNA sequencing (scRNA-seq) have become much more widely available and have achieved unprecedented resolution in revealing cell heterogeneity. The extent of intrinsic cell-to-cell variability in gene expression, or single cell expression variability (scEV), has thus been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what its implications are for multi-cellular organisms. We therefore analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs). For each of the three cell types, we estimated scEV in homogeneous populations of cells; we identified 465, 466, and 291 highly variable genes (HVGs), respectively. These HVGs were enriched with specific functions precisely relevant to the cell types, from which the scRNA-seq data used to identify HVGs were generated--e.g., HVGs identified in lymphoblastoid cells were enriched in cytokine signaling pathways, LAECs collagen formation, and DFs keratinization. HVGs were deeply embedded in gene regulatory networks specific to corresponding cell types. We also found that scEV is a heritable trait, partially determined by cell donors genetic makeups. Furthermore, across genes, especially immune genes, levels of scEV and between-individual variability in gene expression were positively correlated, suggesting a potential link between the two variabilities measured at different organizational levels. Taken together, our results support the \"variation is function\" hypothesis, which postulates that scEV is required for higher-level system function. Thus, we argue that quantifying and characterizing scEV in relevant cell types may deepen our understating of normal as well as pathological cellular processes.

genetics↗

bioRxiv2019

Single-cell RNA sequencing of a European and an African lymphoblastoid cell line

In biomedical research, lymphoblastoid cell lines (LCLs), often established by in vitro infection of resting B cells with Epstein Barr Virus, are commonly used as surrogates for peripheral blood lymphocytes. Genomic and transcriptomic information on LCLs has been used to study the impact of genetic variation on gene expression in humans. Here we present single-cell RNA sequencing (scRNA-seq) data on GM12878 and GM18502--two LCLs derived from the blood of female donors of European and African ancestry, respectively. Cells from three samples (the two LCLs and a 1:1 mixture of the two) were prepared separately using a 10X Genomics Chromium Controller and deeply sequenced. The final dataset contained 7,045 cells from GM12878, 5,189 from GM18502, and 5,820 from the mixture, offering valuable information on single-cell gene expression in highly homogenous cell populations. This dataset is a suitable reference of population differentiation in gene expression at the single-cell level. Data from the mixture provides additional valuable information facilitating the development of statistical methods for data normalization and batch effect correction.

genetics↗

bioRxiv2017

Zebrafish differentially process colour across visual space to match natural scenes

Animal eyes evolve to process behaviourally important visual information, but how retinas deal with statistical asymmetries in visual space remains poorly understood. Using hyperspectral imaging in the field, in-vivo 2-photon imaging of retinal neurons and anatomy, here we show that larval zebrafish use a highly anisotropic retina to asymmetrically survey their natural visual world. First, different neurons dominate different parts of the eye, and are linked to a systematic shift in inner retinal function: Above the animal, there is little colour in nature and retinal circuits are largely achromatic. Conversely, the lower visual field and horizon are colour-rich, and are predominately surveyed by chromatic and colour-opponent circuits that are spectrally matched to the dominant chromatic axes in nature. Second, above the frontal horizon, a high-gain ultraviolet-system piggy-backs onto retinal circuits, likely to support prey-capture. Our results demonstrate high functional diversity among single genetically and morphologically defined types of neurons.

neuroscience↗