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Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows
Author(s) -
Voith von Voithenberg Lena,
Kashyap Aditya,
Opitz Lennart,
Aquino Catharine,
Sykes Timothy,
Nieser Maike,
Petrini Lorenzo F. T.,
Enrriquez Casimiro Nadia,
Kooten Xander F.,
Biskup Saskia,
Schlapbach Ralph,
Schraml Peter,
Kaigala Govind V.
Publication year - 2021
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.202007901
Subject(s) - microscale chemistry , workflow , computational biology , biology , context (archaeology) , computer science , database , paleontology , mathematics education , mathematics
In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow—GeneScape—is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location‐specific drivers.