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Spatial transcriptomics of fixed tissue micro-regions using Pick-Seq [tonsil]

ABSTRACT: Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology. Here we describe a simple method for transcriptional profiling of formalin fixed histology specimens based on mechanical isolation of tissue micro-regions containing 5-20 cells. Sequencing micro-regions from an archival melanoma specimen having multiple distinct histologies reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immunoediting and parallel imaging confirms changes in immuno-phenotypes and cancer cell states.

ORGANISM(S): Homo sapiens

PROVIDER: GSE158563 | GEO | 2021/09/01

REPOSITORIES: GEO

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Spatial transcriptomics of fixed tissue micro-regions using Pick-Seq [melanoma]

Project description:Spatial transcriptomics and multiplexed imaging are complementary methods for studying tissue biology. Here we describe a simple method for transcriptional profiling of formalin fixed histology specimens based on mechanical isolation of tissue micro-regions containing 5-20 cells. Sequencing micro-regions from an archival melanoma specimen having multiple distinct histologies reveals significant differences in transcriptional programs associated with tumor invasion, proliferation, and immunoediting and parallel imaging confirms changes in immuno-phenotypes and cancer cell states.

2021-09-01 | GSE158562 | GEO

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Spatial transcriptomics of fixed tissue micro-regions using Pick-Seq [tonsil]

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Spatial transcriptomics of fixed tissue micro-regions using Pick-Seq

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Spatial transcriptomics of fixed tissue micro-regions using Pick-Seq [breast]

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Spatial transcriptomics of fixed tissue micro-regions using Pick-Seq [melanoma]

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SnFFPE-Seq: towards scalable single nucleus RNA-seq of formalin-fixed paraffin-embedded (FFPE) tissues

Project description:Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.

2022-08-31 | GSE211797 | GEO

Human brain FFPE proteomics: Epilepsy vs Control

Project description:Label-free proteomics of human brain FFPE archival tissue. Comparison of proteome composition between epilepsy and controls across 3 brain regions.

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Robust Gene Expression Signature from Formalin-Fixed Paraffin-Embedded Samples Predicts Prognosis of Non-Small-Cell Lung Cancer Patients

Project description:The requirement of frozen tissues for microarray experiments limits the clinical usage of genome-wide expression profiling using microarray technology. The goal of this study is to test the feasibility of developing lung cancer prognosis gene signatures using genome-wide expression profiling of formalin-fixed paraffin-embedded (FFPE) samples, which are widely available and provide a valuable rich source for studying the association of molecular changes in cancer and associated clinical outcomes. Keywords: Lung Cancer Prognosis, Gene Expression Signature, Formalin Fixed Paraffin Embedded Samples We micro dissected tumor area from FFPE specimen, and used Affymetrix U133 plus 2.0 arrays to obtain gene expression data.

2011-08-31 | E-GEOD-29013 | biostudies-arrayexpress

Automated array-CGH optimized for archival formalin-fixed, paraffin-embedded tumor material

Project description:BACKGROUND: Array Comparative Genomic Hybridization (aCGH) is a rapidly evolving technology that still lacks complete standardization. Yet, it is of great importance to obtain robust and reproducible data to enable meaningful multiple hybridization comparisons. Special difficulties arise when aCGH is performed on archival formalin-fixed, paraffin-embedded (FFPE) tissue due to its variable DNA quality. Recently, we have developed an effective DNA quality test that predicts suitability of archival samples for BAC aCGH. METHODS: In this report, we first used DNA from a cancer cell-line (SKBR3) to optimize the aCGH protocol for automated hybridization, and subsequently optimized and validated the procedure for FFPE breast cancer samples. We aimed for highest throughput, accuracy, and reproducibility applicable to FFPE samples, which can also be important in future diagnostic use. RESULTS: Our protocol of automated array-CGH on archival FFPE ULS-labeled DNA showed very similar results compared with published data and our previous manual hybridization method. CONCLUSION: This report combines automated aCGH on unamplified archival FFPE DNA using non-enzymatic ULS labeling, and describes an optimized protocol for this combination resulting in improved quality and reproducibility.

2007-02-23 | GSE7122 | GEO

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