arrow
Return

Massively parallel nanowell-based single-cell gene expression profiling

delete2017-07-07
delete104
delete
OA
AI
L
Leonard D. Goldstein
Y
Ying-Jiun Jasmine Chen
J
Jude Dunne
A
Alain Mir
H
Hermann Hubschle
J
Joseph Guillory
W
Wenlin Yuan
J
Jingli Zhang
J
Jeremy Stinson
B
Bijay S. Jaiswal
K
Kanika Bajaj Pahuja
I
Ishminder K. Mann
T
Thomas Schaal
L
Leo Li‐Ying Chan
S
Sangeetha Anandakrishnan
C
Chun-wah Lin
P
Patricio Espinoza
S
Syed Akhtar Husain
H
Harris Shapiro
S
Sherry H.-Y. Wei
M
Maithreyan Srinivasan *
S
Somasekar Seshagiri
Z
Zora Modrušan *
DOI:10.1186/s12864-017-3893-1delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Background: Technological advances have enabled transcriptome characterization of cell types at the single-cell level providing new biological insights. New methods that enable simple yet high-throughput single-cell expression profiling are highly desirable. Results: Here we report a novel nanowell-based single-cell RNA sequencing system, ICELL8, which enables processing of thousands of cells per sample. The system employs a 5,184-nanowell-containing microchip to capture similar to 1,300 single cells and process them. Each nanowell contains preprinted oligonucleotides encoding poly-d(T), a unique well barcode, and a unique molecular identifier. The ICELL8 system uses imaging software to identify nanowells containing viable single cells and only wells with single cells are processed into sequencing libraries. Here, we report the performance and utility of ICELL8 using samples of increasing complexity from cultured cells to mouse solid tissue samples. Our assessment of the system to discriminate between mixed human and mouse cells showed that ICELL8 has a low cell multiplet rate (< 3%) and low cross-cell contamination. We characterized single-cell transcriptomes of more than a thousand cultured human and mouse cells as well as 468 mouse pancreatic islets cells. We were able to identify distinct cell types in pancreatic islets, including alpha, beta, delta and gamma cells. Conclusions: Overall, ICELL8 provides efficient and cost-effective single-cell expression profiling of thousands of cells, allowing researchers to decipher single-cell transcriptomes within complex biological samples.
Keywords:
Single cell profiling
RNA sequencing
Single-cell transcriptome
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

BMC Genomics cover
BMC Genomics
IF:
3.7
Papers:
1.9W
Citations:
5.2W

Organization

R
roche holding usa
Scholars:
3.4K
Papers: 2.0K
Citations: 2
G
Genentech
Scholars:
6.6K
Papers: 3.7K
Citations: 1.2K
R
roche holding
Scholars:
2.1W
Papers: 1.1W
Citations: 9
researcher View more organizations