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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Opto-combinatorial indexing enables high-content transcriptomics by linking cell images and transcriptomes.

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We developed a novel method linking cell behavior to single-cell RNA sequencing (scRNA-seq) using optical indices and DNA barcodes. This technique accurately connects cell images with gene expression data for deeper biological insights.

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Area of Science:

  • Biotechnology
  • Genomics
  • Cell Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
  • Linking scRNA-seq data with dynamic cellular phenotypes remains a significant challenge in biological research.

Purpose of the Study:

  • To introduce an integrated analysis method that links cellular phenotypic behavior with scRNA-seq data.
  • To enable precise correlation between cell morphology/behavior and gene expression profiles at the single-cell level.

Main Methods:

  • Utilized a combination of optical indices from cells and hydrogel beads to create 'joint colour codes'.
  • Matched optical combinations from epi-fluorescence microscopy with DNA molecular barcodes from cell-hydrogel bead pairs.
  • Employed next-generation sequencing to analyze concatenated DNA molecular barcodes.

Main Results:

  • Successfully demonstrated an accurate link between cell images and scRNA-seq data through mixed species experiments and longitudinal cell tagging.
  • Extended the approach to multiplexed chemical transcriptomics, identifying distinct phenotypic behaviors in HeLa cells treated with paclitaxel.
  • Determined gene regulation associated with multipolar spindle formation in response to paclitaxel treatment.

Conclusions:

  • The developed method provides a robust platform for integrating cellular phenotypic information with scRNA-seq.
  • This approach facilitates the study of genotype-phenotype relationships and cellular responses to stimuli.
  • Offers new possibilities for high-throughput analysis of cell behavior and gene expression in various biological contexts.