Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 57,521 bioRxiv papers from 264,855 authors.
Most downloaded bioRxiv papers, since beginning of last month
56,141 results found. For more information, click each entry to expand.
367 downloads genomics
Cellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.
366 downloads neuroscience
Tanya L Daigle, Linda Madisen, Travis A Hage, Matthew T Valley, Ulf Knoblich, Rylan S Larsen, Marc M Takeno, Lawrence Huang, Hong Gu, Rachael Larsen, Maya Mills, Alice Bosma-Moody, La'Akea Siverts, Miranda Walker, Lucas T Graybuck, Zizhen Yao, Olivia Fong, Emma Garren, Garreck Lenz, Mariya Chavarha, Julie Pendergraft, James Harrington, Karla E Hirokawa, Julie A Harris, Medea McGraw, Douglas R Ollerenshaw, Kimberly Smith, Baker A Baker, Jonathan T Ting, Susan M Sunkin, Jerome Lecoq, Michael Z Lin, Edward S Boyden, Gabe J Murphy, Nuno da Costa, Jack Waters, Lu Li, Bosiljka Tasic, Hongkui Zeng
Modern genetic approaches are powerful in providing access to diverse types of neurons within the mammalian brain and greatly facilitating the study of their function. We here report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically-defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.
366 downloads genomics
Heini Maaret Natri, Katalina S Bobowik, Pradiptajati Kusuma, Chelzie Crenna Darusallam, Guy S Jacobs, Georgi Hudjashov, J. Stephen Lansing, Herawati Sudoyo, Nicholas E Banovich, Murray Cox, Irene Gallego Romero
Indonesia is the world's fourth most populous country, host to striking levels of human diversity, regional patterns of admixture, and varying degrees of introgression from both Neanderthals and Denisovans. However, it has been largely excluded from the human genomics sequencing boom of the last decade. To serve as a benchmark dataset of molecular phenotypes across the region, we generated genome-wide CpG methylation and gene expression measurements in over 100 individuals from three locations that capture the major genomic and geographical axes of diversity across the Indonesian archipelago. Investigating between- and within-island differences, we find up to 10% of tested genes are differentially expressed between the islands of Mentawai (Sumatra) and New Guinea. Variation in gene expression is closely associated with DNA methylation, with expression levels of 9.7% of genes strongly correlating with nearby CpG methylation, and many of these genes being differentially expressed between islands. Genes identified in our differential expression and methylation analyses are enriched in pathways involved in immunity, highlighting Indonesia tropical role as a source of infectious disease diversity and the strong selective pressures these diseases have exerted on humans. Finally, we identify robust within-island variation in DNA methylation and gene expression, likely driven by very local environmental differences across sampling sites. Together, these results strongly suggest complex relationships between DNA methylation, transcription, archaic hominin introgression and immunity, all jointly shaped by the environment. This has implications for the application of genomic medicine, both in critically understudied Indonesia and globally, and will allow a better understanding of the interacting roles of genomic and environmental factors shaping molecular and complex phenotypes.
364 downloads genomics
Nicholas Schaum, Benoit Lehallier, Oliver Hahn, Shayan Hosseinzadeh, Song E Lee, Rene Sit, Davis P Lee, Patricia Morán Losada, Macy E Zardeneta, Róbert Pálovics, Tobias Fehlmann, James Webber, Aaron McGeever, Hui Zhang, Daniela Berdnik, Weilun Tan, Alexander Zee, Michelle Tan, The Tabula Muris Consortium, Angela Pisco, Jim Karkanias, Norma F. Neff, Andreas Keller, Spyros Darmanis, Stephen R. Quake, Tony Wyss-Coray
Aging is the single greatest cause of disease and death worldwide, and so understanding the associated processes could vastly improve quality of life. While the field has identified major categories of aging damage such as altered intercellular communication, loss of proteostasis, and eroded mitochondrial function, these deleterious processes interact with extraordinary complexity within and between organs. Yet, a comprehensive analysis of aging dynamics organism-wide is lacking. Here we performed RNA-sequencing of 17 organs and plasma proteomics at 10 ages across the mouse lifespan. We uncover previously unknown linear and non-linear expression shifts during aging, which cluster in strikingly consistent trajectory groups with coherent biological functions, including extracellular matrix regulation, unfolded protein binding, mitochondrial function, and inflammatory and immune response. Remarkably, these gene sets are expressed similarly across tissues, differing merely in age of onset and amplitude. Especially pronounced is widespread immune cell activation, detectable first in white adipose depots in middle age. Single-cell RNA-sequencing confirms the accumulation of adipose T and B cells, including immunoglobulin J-expressing plasma cells, which also accrue concurrently across diverse organs. Finally, we show how expression shifts in distinct tissues are highly correlated with corresponding protein levels in plasma, thus potentially contributing to aging of the systemic circulation. Together, these data demonstrate a similar yet asynchronous inter- and intra-organ progression of aging, thereby providing a foundation to track systemic sources of declining health at old age.
364 downloads cancer biology
Metastasis is the main cause of death in cancer patients but remains a poorly understood process. Small cell lung cancer (SCLC) is one of the most lethal and most metastatic types of human cancer. SCLC cells normally express neuroendocrine and neuronal gene programs but accumulating evidence indicates that these cancer cells become relatively more neuronal and less neuroendocrine as they gain the ability to metastasize. Here we show that mouse and human SCLC cells in culture and in vivo can grow cellular protrusions that resemble axons. The formation of these protrusions is controlled by multiple neuronal factors implicated in axonogenesis, axon guidance, and neuroblast migration. Disruption of these axon-like protrusions impairs cell migration in culture and inhibits metastatic ability in vivo. The co-option of developmental neuronal programs is a novel molecular and cellular mechanism that contributes to the high metastatic ability of SCLC.
362 downloads neuroscience
Julia TCW, Shuang A Liang, Lu Qian, Nina H. Pipalia, Michael J Chao, Yang Shi, Sarah E Bertelsen, Manav Kapoor, Edoardo Marcora, Elizabeth Sikora, David M Holtzman, Frederick R. Maxfield, Bin Zhang, Minghui Wang, Wayne W. Poon, Alison A. Goate
Apolipoprotein E ( APOE ) ε4 is the strongest genetic risk factor for Alzheimer's disease (AD). Although its association with AD is well-established, the impact of APOE ε4 on human brain cell function remains unclear. Here we investigated the effects of APOE ε4 on several brain cell types derived from human induced pluripotent stem cells and human APOE targeted replacement mice. Gene set enrichment and pathway analyses of whole transcriptome profiles showed that APOE ε4 is associated with dysregulation of cholesterol homeostasis in human but not mouse astrocytes and microglia. Elevated matrisome signaling associated with chemotaxis, glial activation and lipid biosynthesis in APOE ε4 mixed neuron/astrocyte cultures parallels altered pathways uncovered in cell-type deconvoluted transcriptomic data from APOE ε4 glia and AD post-mortem brains. Experimental validation of the transcriptomic findings showed that isogenic APOE ε4 is associated with increased lysosomal cholesterol levels and decreased cholesterol efflux, demonstrating decoupled lipid metabolism. APOE ε4 glia also secrete higher levels of proinflammatory chemokines, cytokines and growth factors, indicative of glial activation. Thus, APOE ε4 induces human glia-specific dysregulation that may initiate AD risk.
362 downloads genomics
Background: The inherited susceptibility of common, complex diseases may be caused by rare, 'monogenic' pathogenic variants or by the cumulative effect of numerous common, 'polygenic' variants. As such, comprehensive genome interpretation could involve two distinct genetic testing technologies -- high coverage next generation sequencing for known genes to detect pathogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). Here we assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. Methods: First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for three common diseases -- coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF) -- calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in 11,502 European individuals seeking genetic testing. Results: We found imputation accuracy r2 values of greater than 0.90 for all ten samples -- including those of African and Ashkenazi Jewish ancestry -- with lcWGS data at 0.5X. GPSs calculated using both lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the three common diseases (r2 = 0.93 - 0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, including South Asian, East Asian, and Hispanic individuals, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 European individuals, confirming odds ratios per standard deviation increment in GPSs ranging 1.28 to 1.59, consistent with previous studies. Conclusions: Here we show that lcWGS is an alternative approach to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.
361 downloads cell biology
Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two approaches for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; by solving partial differential equations on a graph representing discrete cell state relationships, and by solving the equations on a continuous cell state-space. We demonstrate how to calibrate model parameters from single or multiple time-point single-cell sequencing data, and examine the effects of data processing algorithms on the model calibration and predictions. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell types during the pathogenesis of acute myeloid leukemia. The mathematical modeling framework we present is general and can be applied to study cell state-transitions in any single-cell genome sequencing dataset.
360 downloads neuroscience
The recent availability of large-scale neuroimaging cohorts (here the UK Biobank [UKB] and the Human Connectome Project [HCP]) facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. We tested the association between 654,386 vertex-wise measures of cortical and subcortical morphology (from T1w and T2w MRI images) and behavioural, cognitive, psychiatric and lifestyle data. We found a significant association of grey-matter structure with 58 out of 167 UKB phenotypes spanning substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains (UKB discovery sample: N=9,888). Twenty-three of the 58 associations replicated (UKB replication sample: N=4,561; HCP, N=1,110). In addition, differences in body size (height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the association, providing possible insight into previous MRI case-control studies for psychiatric disorders where case status is associated with body mass index. Using the same linear mixed model, we showed that most of the associated characteristics (e.g. age, sex, body size, diabetes, being a twin, maternal smoking, body size) could be significantly predicted using all the brain measurements in out-of-sample prediction. Finally, we demonstrated other applications of our approach including a Region Of Interest (ROI) analysis that retain the vertex-wise complexity and ranking of the information contained across MRI processing options.
360 downloads cell biology
Li Gao, Yvonne Kraus, Maximilian Wranik, Tobias Weinert, Stefanie D. Pritzl, Joyce C.M. Meiring, Rebekkah Bingham, Natacha Olieric, Anna Akhmanova, Theobald Lohmüller, Michel O. Steinmetz, Oliver Thorn-Seshold
Here we present GFP-orthogonal optically controlled reagents for reliable and repetitive in cellulo modulation of microtubule dynamics and its dependent processes. Optically controlled reagents ('photopharmaceuticals') have developed into powerful tools for high-spatiotemporal-precision control of endogenous biology, with numerous applications in neuroscience, embryology, and cytoskeleton research. However, the restricted chemical domain of photopharmaceutical scaffolds has constrained their properties and range of applications. Styrylbenzothiazoles are an as-yet unexplored scaffold for photopharmaceuticals, which we now rationally design to feature potent photocontrol, switching microtubule cytoskeleton function off and on according to illumination conditions. We show more broadly that this scaffold is exceptionally chemically and biochemically robust as well as substituent-tolerant, and offers particular advantages for intracellular biology through a range of desirable photopharmaceutical and drug-like properties not accessible to the current classes of photoswitches. We expect that these reagents will find powerful applications enabling robust, high precision, optically controlled cell biological experimentation in cytoskeleton research and beyond.
359 downloads genomics
The spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 micron spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq's utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.
358 downloads neuroscience
Two-photon microscopy of calcium-dependent sensors has enabled unprecedented recordings from vast populations of neurons. While the sensors and microscopes have matured over several generations of development, computational methods to process the resulting movies remain inefficient and can give results that are hard to interpret. Here we introduce Suite2p: a fast, accurate and complete pipeline that registers raw movies, detects active cells, extracts their calcium traces and infers their spike times. Suite2p runs on standard workstations, operates faster than real time, and recovers ~2 times more cells than the previous state-of-the-art method. Its low computational load allows routine detection of ~10,000 cells simultaneously with standard two-photon resonant-scanning microscopes. Recordings at this scale promise to reveal the fine structure of activity in large populations of neurons or large populations of subcellular structures such as synaptic boutons.
358 downloads developmental biology
Dorin-Mirel Popescu, Rachel A. Botting, Emily Stephenson, Kile Green, Laura Jardine, Emily F Calderbank, Mirjana Efremova, Meghan Acres, Daniel Maunder, Peter Vegh, Issac Goh, Yorick Gitton, Jongeun Park, Krzysztof Polanski, Roser Vento-Tormo, Zhichao Miao, Rachel Rowell, David McDonald, James Fletcher, David Dixon, Elizabeth Poyner, Gary Reynolds, Michael Mather, Corina Moldovan, Lira Mamanova, Frankie Greig, Matthew D Young, Kerstin Meyer, Steven Lisgo, Jaume Bacardit, Andrew Fuller, Ben Millar, Barbara Innes, Susan Lindsay, Michael J.T. Stubbington, Monika D Kowalczyk, Bo D Li, Orr Ashenbrg, Marcin D Tabaka, Danielle Dionne, Timothy L. Tickle, Michal Slyper, Orit Rozenblatt-Rosen, Andrew Filby, Alexandra-Chloe Villani, Anindita Roy, Aviv D Regev, Alain Chedotal, Irene Roberts, Berthold D Gottgens, Elisa Laurenti, Sam Behjati, Sarah D Teichmann, Muzlifah Haniffa
Definitive haematopoiesis in the fetal liver supports self-renewal and differentiation of haematopoietic stem cells/multipotent progenitors (HSC/MPPs), yet remains poorly defined in humans. Using single cell transcriptome profiling of ~133,000 fetal liver and ~65,000 fetal skin and kidney cells, we identify the repertoire of blood and immune cells in first and early second trimesters of development. From this data, we infer differentiation trajectories from HSC/MPPs, and evaluate the impact of tissue microenvironment on blood and immune cell development. We predict coupling of mast cell differentiation with erythro-megakaryopoiesis and identify physiological erythropoiesis in fetal skin. We demonstrate a shift in fetal liver haematopoietic composition during gestation away from being erythroid-predominant, accompanied by a parallel change in HSC/MPP differentiation potential, which we functionally validate. Our integrated map of fetal liver haematopoiesis provides a blueprint for the study of paediatric blood and immune disorders, and a valuable reference for understanding and harnessing the therapeutic potential of HSC/MPPs.
358 downloads neuroscience
Lucas Graybuck, Adriana Sedeño-Cortés, Thuc Nghi Nguyen, Miranda Walker, Eric Szelenyi, Garreck Lenz, La'Akea Sieverts, Tae Kyung Kim, Emma Garren, Brian Kalmbach, Shenqin Yao, Marty Mortrud, John Mich, Jeff Goldy, Kimberly A Smith, Nick Dee, Zizhen Yao, Ali Cetin, Boaz P Levi, Ed Lein, Jonathan Ting, Hongkui Zeng, Tanya Daigle, Bosiljka Tasic
Labeling and perturbation of specific cell types in multicellular systems has transformed our ability to understand them. The rapid pace of cell type identification by new single-cell analysis methods has not been met with efficient access to these newly discovered types. To enable access to specific neural populations in the mouse cortex, we have collected single cell chromatin accessibility data from select cell types. We clustered the single cell data and mapped them to single cell transcriptomics to identify highly specific enhancers for cell subclasses. These enhancers, when cloned into AAVs and delivered to the brain by retro orbital injections, transgene expression in specific cell subclasses throughout the mouse brain. This approach will enable functional investigation of cell types in the mouse cortex and beyond.
358 downloads bioinformatics
The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of ~1 million cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.
357 downloads neuroscience
Basal amygdala (BA) neurons guide associative learning via acquisition of responses to stimuli that predict salient appetitive or aversive outcomes. We examined the learning- and state-dependent dynamics of BA neurons and ventral tegmental area dopamine axons that innervate BA (VTADA→BA) using two-photon imaging and photometry in behaving mice. BA neurons did not respond to arbitrary visual stimuli, but acquired responses to stimuli that predicted either rewards or punishments. Most VTADA→BA axons were activated by both rewards and punishments, and acquired responses to cues predicting these outcomes during learning. Responses to cues predicting food rewards in VTADA→BA axons and BA neurons in hungry mice were strongly attenuated following satiation, while responses to cues predicting unavoidable punishments persisted or increased. Therefore, VTADA→BA axons may provide a reinforcement signal of motivational salience that invigorates adaptive behaviors by promoting learned responses to appetitive or aversive cues in distinct, intermingled sets of BA excitatory neurons.
357 downloads genetics
Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group, the prediction accuracy of polygenic scores depends on characteristics such as the age or sex composition of the individuals in which the GWAS and the prediction were conducted, and on the GWAS study design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
357 downloads bioinformatics
Background: Metagenomic sequencing has lead to the recovery of previously unexplored microbial genomes. In this sense, short-reads sequencing platforms often result in highly fragmented metagenomes, thus complicating downstream analyses. Third generation sequencing technologies, such as MinION, could lead to more contiguous assemblies due to their ability to generate long reads. Nevertheless, there is a lack of studies evaluating the suitability of the available assembly tools for this new type of data. Findings: We benchmarked the ability of different short-reads and long-reads tools to assembly two different commercially available mock communities, and observed remarkable differences in the resulting assemblies depending on the software of choice. Short-reads metagenomic assemblers proved unsuitable for MinION data. Among the long-reads assemblers tested, Flye and Canu were the only ones performing well in all the datasets. These tools were able to retrieve complete individual genomes directly from the metagenome, and assembled a bacterial genome in only two contigs in the best scenario. Despite the intrinsic high error of long-reads technologies, Canu and Flye lead to high accurate assemblies (~99.4-99.8 % of accuracy). However, errors still had an impact on the prediction of biosynthetic gene clusters. Conclusions: MinION metagenomic sequencing data proved sufficient for assembling low-complex microbial communities, leading to the recovery of highly complete and contiguous individual genomes. This work is the first systematic evaluation of the performance of different assembly tools on MinION data, and may help other researchers willing to use this technology to choose the most appropriate software depending on their goals. Future work is still needed in order to assess the performance of Oxford Nanopore MinION data on more complex microbiomes.
357 downloads bioinformatics
To extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer′s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer′s and healthy brain data has been challenging in older adults (age > 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting-edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer′s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer′s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject-level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject-level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.
356 downloads cancer biology
Hamad Alshetaiwi, Nicholas Pervolarakis, Laura Lynn McIntyre, Dennis Ma, Quy Nguyen, Jan Akara Rath, Kevin Nee, Grace Hernandez, Katrina Evans, Leona Torosian, Anushka Silva, Craig Walsh, Kai Kessenbrock
Myeloid-derived suppressor cells (MDSCs) are innate immune cells that acquire the capacity to suppress adaptive immune responses during cancer. It remains elusive how MDSCs differ from their normal myeloid counterparts, which limits our ability to specifically detect and therapeutically target MDSCs during cancer. Here, we used single-cell RNAseq to compare MDSC-containing splenic myeloid cells from breast tumor-bearing mice to wildtype controls. Our computational analysis of 14,646 single-cell transcriptomes reveals that MDSCs emerge through a previously unrealized aberrant neutrophil maturation trajectory in the spleen giving rise to a unique chemokine-responsive, immunosuppressive cell state that strongly differs from normal myeloid cells. We establish the first MDSC-specific gene signature and identify novel surface markers for improved detection and enrichment of MDSCs in murine and human samples. Our study provides the first single-cell transcriptional map defining the development of MDSCs, which will ultimately enable us to specifically target these cells in cancer patients.
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