MCB Seminar | Artificial Intelligence for Mass Spectrometry Proteomics Data Analysis

The Molecular and Cellular Biology Seminar series features Emily Davis discussing “It’s tart week”: spatially-resolved transcriptomics on Nov. 9 from 4:35 to 5:55 p.m.

• Contact Dr. Sarah Wyatt , MCB director, for more information.

Davis is a graduate student in Biological Sciences and Molecular and Cellular Biology .

Abstract : Bulk RNA sequencing and single-cell RNA sequencing are widely-used and powerful tools in molecular biology, but they do not provide adequate information about the spatial distribution of expressed genes within a tissue. ^1,2 Hailed as the, “Method of the Year,” in 2020 by Nature Methods , spatially-resolved transcriptomics offers a solution to this issue by allowing visualization and quantitative analysis of the single-cell transcriptome with spatial data. ^1,3 It utilizes in situ hybridization, sequencing, and capturing, followed by computational reconstruction of spatial data. ³ The data produced by spatially-resolved transcriptomics often includes genes that are expressed in a heterogeneous mixture of cell types, and thus are difficult to interpret and analyze. Most current data interpretation methods utilize nuclei staining, and it is difficult to discern boundaries of cells, which can negatively affect downstream data analysis. In order to achieve cellular resolution, cell segmentation must be performed. A program called Baysor, which is used to interpret imaging-based spatially-resolved transcriptomics data, was developed at Harvard Medical School by Petukhov, et. al . Baysor performs cell segmentation on the basis of the likelihood of transcriptional composition and cellular morphology and may be complemented by staining data. Cell segmentation increases the number of cells detected and also provides more informative data for each cell. Baysor performed on par with, if not better, in comparison to previously-published programs for cell segmentation. ⁴ As spatially-resolved transcriptomics gains usage and application, so will the need for programs like Baysor to interpret the generated data to pave the way for the future of therapeutics and diagnostics.

Key references:

Ståhl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics.  Science . 2016;353(6294):78-82. doi:10.1126/science.aaf2403 Crosetto N, Bienko M, van Oudenaarden A. Spatially resolved transcriptomics and beyond.  Nat Rev Genet . 2015;16(1):57-66. doi:10.1038/nrg3832 Marx V. Method of the Year: spatially resolved transcriptomics [published correction appears in Nat Methods. 2021 Feb;18(2):219].  Nat Methods . 2021;18(1):9-14. doi:10.1038/s41592-020-01033-y Petukhov V, Xu RJ, Soldatov RA, et al. Cell segmentation in imaging-based spatial transcriptomics [published online ahead of print, 2021 Oct 14].  Nat Biotechnol . 2021;10.1038/s41587-021-01044-w. doi:10.1038/s41587-021-01044-w