↓ Skip to main content

Q

Overview of attention for article published in BMC Biology, July 2017
Altmetric Badge

Mentioned by

news
7 news outlets
blogs
1 blog
twitter
17 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
151 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Q&A: using Patch-seq to profile single cells
Published in
BMC Biology, July 2017
DOI 10.1186/s12915-017-0396-0
Pubmed ID
Authors

Cathryn R. Cadwell, Rickard Sandberg, Xiaolong Jiang, Andreas S. Tolias

Abstract

Individual neurons vary widely in terms of their gene expression, morphology, and electrophysiological properties. While many techniques exist to study single-cell variability along one or two of these dimensions, very few techniques can assess all three features for a single cell. We recently developed Patch-seq, which combines whole-cell patch clamp recording with single-cell RNA-sequencing and immunohistochemistry to comprehensively profile the transcriptomic, morphologic, and physiologic features of individual neurons. Patch-seq can be broadly applied to characterize cell types in complex tissues such as the nervous system, and to study the transcriptional signatures underlying the multidimensional phenotypes of single cells.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 151 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 26%
Researcher 30 20%
Student > Master 15 10%
Other 9 6%
Professor 7 5%
Other 21 14%
Unknown 29 19%
Readers by discipline Count As %
Neuroscience 63 42%
Agricultural and Biological Sciences 28 19%
Biochemistry, Genetics and Molecular Biology 12 8%
Medicine and Dentistry 5 3%
Psychology 2 1%
Other 6 4%
Unknown 35 23%