A5034356727- Single-cell and spatial transcriptomics
- Extracellular vesicles in disease
- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Cancer-related molecular mechanisms research
- Cancer Genomics and Diagnostics
- RNA Research and Splicing
- RNA modifications and cancer
- Genetics, Aging, and Longevity in Model Organisms
- MicroRNA in disease regulation
- Neuroinflammation and Neurodegeneration Mechanisms
- Biosensors and Analytical Detection
- T-cell and B-cell Immunology
- Molecular Biology Techniques and Applications
- Global Cancer Incidence and Screening
- Immune Cell Function and Interaction
- Adipokines, Inflammation, and Metabolic Diseases
- 3D Printing in Biomedical Research
- Mathematical Biology Tumor Growth
- Cancer Cells and Metastasis
- Receptor Mechanisms and Signaling
- Health disparities and outcomes
- Health, Environment, Cognitive Aging
- Adipose Tissue and Metabolism
National Cancer Institute
2019-2023
National Institutes of Health
2023
University of Pennsylvania
2012-2019
Identifying terminal nematode cells Single-cell RNA sequencing provides the power to identify developmental trajectory of an organism. However, identifying temporal lineage cell development can be difficult without large-scale analyses. Packer et al. sequenced more than 80,000 from embryos roundworm Caenorhabditis elegans determine expression genes directing types. Because all somatic in a C. individual have been mapped, authors are able connect gene with lineages over time during...
Abstract Background RNA-seq is a powerful technique for identifying and quantifying transcription splicing events, both known novel. However, given its recent development the proliferation of library construction methods, understanding bias it introduces incomplete but critical to realizing value. Results We present method, in vitro sequencing (IVT-seq), assessing technical biases generation at scale. created pool over 1,000 transcribed RNAs from full-length human cDNA sequenced them with...
Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing assessment dispersion, nonzero fraction, and other aspects its beyond mean. These statistical characterizations gene are critical for understanding variation selecting marker genes population heterogeneity. However, scRNA-seq data noisy, with cell typically sequenced at low coverage, making it difficult to infer properties from raw counts. Based on a...
Differentiation of metazoan cells requires execution different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within seeming identical phenotype. This brings into question the relationship between states and cell phenotypes. Additionally, transcriptomics presents unique analysis challenges that need to be addressed answer this question. We present high quality deep read-depth RNA sequencing for 91 from five mouse tissues 18 two rat...
Investigation of human CNS disease and drug effects has been hampered by the lack a system that enables single-cell analysis live adult patient brain cells. We developed culturing system, based on papain-aided procedure, for resected tissue removed during neurosurgery. performed transcriptomics over 300 cells, permitting identification oligodendrocytes, microglia, neurons, endothelial astrocytes after 3 weeks in culture. Using deep sequencing, we detected 12,000 expressed genes, including...
Despite the recognized importance of dorsal raphe (DR) serotonergic (5-HT) nuclei in pathophysiology depression and anxiety, molecular components/putative drug targets expressed by these neurons are poorly characterized. Utilizing promoter an ETS domain transcription factor that is a stable marker 5-HT (Pet-1) to drive neuronal expression YFP, we identified live acute slices. We isolated RNA from single ventromedial lateral wings DR performed single-cell RNA-Seq analysis identifying >500...
Brown adipocytes (BAs) are specialized for adaptive thermogenesis and, upon sympathetic stimulation, activate mitochondrial uncoupling protein (UCP)-1 and oxidize fatty acids to generate heat. The capacity brown adipose tissue (BAT) protect against obesity metabolic disease is recognized, yet information about which signals BA, besides β3-adrenergic receptor limited. Using single-cell transcriptomics, we confirmed the presence of mRNAs encoding traditional BAT markers (i.e., UCP1, expressed...
Many R packages have been developed for transcriptome analysis but their use often requires familiarity with and integrating results of different scripts to wrangle the datatypes. Furthermore, exploratory data analyses generate multiple derived datasets such as subsets or transformations, which can be difficult track. Here we present PIVOT, an R-based platform that wraps open source a uniform user interface graphical management allows non-programmers interactively explore transcriptomics...
Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell sequencing (scRNA-seq) have received considerable attention, but the broad reliability methods and factors governing their performance are still poorly known.
Abstract Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing assessment dispersion, burstiness, and other aspects its beyond mean. These statistical characterizations gene are critical for understanding variation selecting marker genes population heterogeneity. However, scRNA-seq data is noisy, with cell typically sequenced at low coverage, making it difficult to infer properties from raw counts. Based on a...
Abstract Rapid advances in massively parallel single cell RNA sequencing (scRNA-seq) is paving the way for high-resolution profiling of biological samples. In most scRNA-seq studies, only a small fraction transcripts present each are sequenced. The efficiency, that is, proportion sequenced, can be especially low highly parallelized experiments where number reads allocated small. This leads to unreliable quantification lowly and moderately expressed genes, resulting extremely sparse data...
Abstract C. elegans is an animal with few cells, but a striking diversity of cell types. Here, we characterize the molecular basis for their specification by profiling transcriptomes 84,625 single embryonic cells. We identify 284 terminal and pre-terminal types, mapping most to exact position in elegans’ invariant lineage. use these annotations perform first quantitative analysis relationship between lineage transcriptome whole organism. find that strong lineage-transcriptome correlation...
Abstract The development of single cell RNA sequencing technologies has emerged as a powerful means profiling the transcriptional behavior cells, leveraging breadth measurements to make inferences about type. However, there is still little understanding how well these methods perform at measuring variability for small sets genes and what “transcriptome coverage” (e.g. detected per cell) needed accurate measurements. Here, we use molecule FISH 26 in thousands melanoma cells provide an...
Abstract Background RNA sequencing (RNA-seq) is a powerful technique for identifying and quantifying transcription splicing events, both known novel. However, given its recent development the proliferation of library construction methods, understanding bias it introduces incomplete but critical to realizing value. Results Here we present method, in vitro (IVT-seq), assessing technical biases RNA-seq generation at scale. We created pool > 1000 transcribed (IVT) RNAs from full-length human...
Abstract Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell sequencing (scRNA-seq) have received considerable attention, but the broad reliability methods and factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess transfer function three scRNA-seq modulating function. All detected greater than 70% expected number genes had 50% probability detecting with abundance 2...
Abstract Many R packages have been developed for transcriptome analysis but their use often requires familiarity with and integrating results of different is difficult. Here we present PIVOT, an R-based application a uniform user interface graphical data management that allows non-programmers to conveniently access various bioinformatics tools interactively explore transcriptomics data. PIVOT supports many popular open source provides extensive set statistical manipulations. A graph-based...