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Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues.
Nat Methods
January 2025
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
The phenotypic and functional states of cells are modulated by a complex interactive molecular hierarchy of multiple omics layers, involving the genome, epigenome, transcriptome, proteome and metabolome. Spatial omics approaches have enabled the study of these layers in tissue context but are often limited to one or two modalities, offering an incomplete view of cellular identity. Here we present spatial-Mux-seq, a multimodal spatial technology that allows simultaneous profiling of five different modalities: two histone modifications, chromatin accessibility, whole transcriptome and a panel of proteins at tissue scale and cellular level in a spatially resolved manner.
View Article and Find Full Text PDFAuthor Correction: Multi-omic and single-cell profiling of chromothriptic medulloblastoma reveals genomic and transcriptomic consequences of genome instability.
Nat Commun
January 2025
Group Genome Instability in Tumors, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.
A quantitative prediction method utilizing whole omics data for biosensing.
Sci Rep
January 2025
BioResource Research Center, RIKEN, 3-1-1, Koyadai, Tsukuba, 305-0074, Ibaraki, Japan.
Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker.
View Article and Find Full Text PDFIntegrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.
Nat Commun
January 2025
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. Here we develop Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN) to identify lineage-informative genes or subtrees where lineage and expression are tightly coupled.
View Article and Find Full Text PDFComprehensive guide for epigenetics and transcriptomics data quality control.
STAR Protoc
January 2025
Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA. Electronic address:
Host response to environmental exposures such as pathogens and chemicals can include modifications to the epigenome and transcriptome. Improved signature discovery, including the identification of the agent and timing of exposure, has been enabled by advancements in assaying techniques to detect RNA expression, DNA base modifications, histone modifications, and chromatin accessibility. The interrogation of the epigenome and transcriptome cascade requires analyzing disparate datasets from multiple assay types, often at single-cell resolution, derived from the same biospecimen.
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