PHLI-seq
Cited by: 11
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Update date: 2018-10-10

Description

PHLI-seq: constructing and visualizing cancer genomic maps in 3D by phenotype-based high-throughput laser-aided isolation and sequencing. Spatial mapping of genomic data to tissue context in a high-throughput and high-resolution manner has been challenging due to technical limitations. Here, we describe PHLI-seq, a novel approach that enables high-throughput isolation and genome-wide sequence analysis of single cells or small numbers of cells to construct genomic maps within cancer tissue in relation to the images or phenotypes of the cells. By applying PHLI-seq, we reveal the heterogeneity of breast cancer tissues at a high resolution and map the genomic landscape of the cells to their corresponding spatial locations and phenotypes in the 3D tumor mass. The online version of this article (10.1186/s13059-018-1543-9) contains supplementary material, which is available to authorized users.

Keywords

Spatial Genomics
PHLI-seq

Related


1

PHLI-seq: constructing and visualizing cancer genomic maps in 3D by phenotype-based high-throughput laser-aided isolation and sequencing.

Authors: Kim, Sungsik; Lee, Amos Chungwon; Lee, Han-Byoel; Kim, Jinhyun; Jung, Yushin; Ryu, Han Suk; Lee, Yongju; Bae, Sangwook; Lee, Minju; Lee, Kyungmin; Kim, Ryong Nam; Park, Woong-Yang; Han, Wonshik; Kwon, Sunghoon
Journal: Genome Biol,2018/10/08;19(1):158.
Keywords:Spatial Genomics; PHLI-seq; Breast cancer; Cell isolation; Precision oncology; Single-cell sequencing; Spatially resolved sequencing; Tumor heterogeneity
IF: 17.906
Cited by: 13

2

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows.

Authors: Voith von Voithenberg, Lena; Kashyap, Aditya; Opitz, Lennart; Aquino, Catharine; Sykes, Timothy; Nieser, Maike; Petrini, Lorenzo F T; Enrriquez Casimiro, Nadia; van Kooten, Xander F; Biskup, Saskia; Schlapbach, Ralph; Schraml, Peter; Kaigala, Govind V
Journal: Small,2021/06;17(23):e2007901.
Keywords:Spatial Genomics; LCM-seq; microfluidic probe; next generation sequencing; spatial genomics; tumor heterogeneity
IF: 15.153
Cited by: 3

Tool types

Data Visualisation
Exploratory Data Analysis

Languages

Python