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Single-cell omics technologies has extended beyond the transcriptome to profile diverse physical-chemical properties at single-cell resolution, including whole genomes/exomes, DNA methylation, chromatin accessibility, histone modifications, epitranscriptome (e.g., mRNAs, microRNAs, tRNAs, lncRNAs), proteome, phosphoproteome, metabolome, and more.
Associating the barcodes with each mRNA sequence provides a spatial transcriptomics map of the tissue. While this is not a single-cell methodology, the 10 uM channels capture only 1-2 cells per square, generating near-single-cell resolution. The ADT sequences capture spatial proteomic information that can be compared to the transcriptomic data.
Spatial transcriptomics is a method that captures positional context of transcriptional activity within intact tissue [1].The historical precursor to spatial transcriptomics is in situ hybridization [2], where the modernized omics terminology refers to the measurement of all the mRNA in a cell rather than select RNA targets.
Related to Single-cell multiomics is the field of Spatial Omics which assays tissues through omics readouts that preserve the relative spatial orientation of the cells in the tissue. The number of Spatial Omics methods published still lags behind the number of methods published for Single-Cell multiomics, but the numbers are catching up (Single ...
Tomomics: A combination of tomography and omics methods to understand tissue or cell biochemistry at high spatial resolution, typically using imaging mass spectrometry data. [26] Viral metagenomics: Using omics methods in soil, ocean water, and humans to study the Virome and Human virome.
A list of more than 100 different single-cell omics methods has been ... Although LCM preserves the knowledge of the spatial location of a sampled cell within a ...
Detecting differences in gene expression level between two populations is used both single-cell and bulk transcriptomic data. Specialised methods have been designed for single-cell data that considers single cell features such as technical dropouts and shape of the distribution e.g. Bimodal vs. unimodal. [23]
This single cell shows the process of the central dogma of molecular biology, which are all steps researchers are interested to quantify (DNA, RNA, and Protein).. In cell biology, single-cell analysis and subcellular analysis [1] refer to the study of genomics, transcriptomics, proteomics, metabolomics, and cell–cell interactions at the level of an individual cell, as opposed to more ...