Spatial organization of cell types in the mouse cortex revealed by multiplexed single-molecule fluorescent in situ hybridization

Author(s):  
Lars Borm
2019 ◽  
Author(s):  
Kristen R. Maynard ◽  
Madhavi Tippani ◽  
Yoichiro Takahashi ◽  
BaDoi N. Phan ◽  
Thomas M. Hyde ◽  
...  

ABSTRACTMultiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.


2020 ◽  
Vol 48 (11) ◽  
pp. e66-e66 ◽  
Author(s):  
Kristen R Maynard ◽  
Madhavi Tippani ◽  
Yoichiro Takahashi ◽  
BaDoi N Phan ◽  
Thomas M Hyde ◽  
...  

Abstract Multiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.


1999 ◽  
Vol 65 (4) ◽  
pp. 1746-1752 ◽  
Author(s):  
Cleber C. Ouverney ◽  
Jed A. Fuhrman

ABSTRACT We propose a novel method for studying the function of specific microbial groups in situ. Since natural microbial communities are dynamic both in composition and in activities, we argue that the microbial “black box” should not be regarded as homogeneous. Our technique breaks down this black box with group-specific fluorescent 16S rRNA probes and simultaneously determines 3H-substrate uptake by each of the subgroups present via microautoradiography (MAR). Total direct counting, fluorescent in situ hybridization, and MAR are combined on a single slide to determine (i) the percentages of different subgroups in a community, (ii) the percentage of total cells in a community that take up a radioactively labeled substance, and (iii) the distribution of uptake within each subgroup. The method was verified with pure cultures. In addition, in situ uptake by members of the α subdivision of the class Proteobacteria(α-Proteobacteria) and of the Cytophaga-Flavobacteriumgroup obtained off the California coast and labeled with fluorescent oligonucleotide probes for these subgroups showed that not only do these organisms account for a large portion of the picoplankton community in the sample examined (∼60% of the universal probe-labeled cells and ∼50% of the total direct counts), but they also are significant in the uptake of dissolved amino acids in situ. Nearly 90% of the total cells and 80% of the cells belonging to the α-Proteobacteria and Cytophaga-Flavobacterium groups were detectable as active organisms in amino acid uptake tests. We suggest a name for our triple-labeling technique, substrate-tracking autoradiographic fluorescent in situ hybridization (STARFISH), which should aid in the “dissection” of microbial communities by type and function.


2020 ◽  
Author(s):  
Llilians Calvo ◽  
Matthew Ronshaugen ◽  
Tom Pettini

ABSTRACTRecently, advances in fluorescent in-situ hybridization techniques and in imaging technology have enabled visualisation and counting of individual RNA molecules in single cells. This has greatly enhanced the resolution in our understanding of transcriptional processes. Here, we adapt a recently published smiFISH protocol (single-molecule inexpensive fluorescent in-situ hybridization) to whole embryos across a range of arthropod model species, and also to non-embryonic tissues. Using multiple fluorophores with distinct spectra and white light laser confocal imaging, we simultaneously detect and separate single RNAs from up to eight different genes in a whole embryo. We also combine smiFISH with cell membrane immunofluorescence, and present an imaging and analysis pipeline for 3D cell segmentation and single-cell RNA counting in whole blastoderm embryos. Finally, using whole embryo single-cell RNA count data, we propose two alternative single-cell variability measures to the commonly used Fano factor, and compare the capacity of these three measures to address different aspects of single-cell expression variability.


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