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Integrating Protein and Transcript Expression

An important principle in our work is to maximize the information we get from precious patient specimens. To that end, technologies that simultaneously provide protein and transcript information from the same single cells are critical.

I've contributed to two recently published papers that demonstrate the potential power of integration... It's worth highlighting them.

The first was a study led by Diane Bolton at the US Military HIV Research Program. Diane and her team performed in-depth and integrated phenotyping of cells infected with SIV (the monkey model for HIV). By combining transcriptomic analysis with cell surface flow cytometry, they identified changes in protein expression associated with different phases of the viral life cycle (as defined by expression of particular SIV mRNA). The underlying technology is pretty accessible... it combines fluorescence activated cell sorting with 96-plex qPCR (using the Fluidigm BioMark system). This could be an interesting approach to better understanding other intracellular pathogens, and how they modulate their host cells. PLoS Pathogens, June 2017 (click on the figure to jump to the paper):

Integrating Protein and Transcript Analysis for Virally Infected Cells

The second paper involves a technology that we're bringing to the lab at NYU. Marlon Stoeckius, and his colleagues at the New York Genome Center, have developed a new platform that tags antibodies with oligonucleotides instead of fluorescent dyes. Cells are stained using protocols similar to flow cytometry, but then they are encapsulated into single cell droplets, and single cell RNA sequencing is performed to read antibody-oligo tags and obtain whole transcriptome information - simultaneously for each cell. The platform is adapted for the common 10X Genomics systems (available in many sequencing cores), and has relatively high throughput. I met Marlon at a meeting, and we realized that it would be great to benchmark his technology against multiparameter flow cytometry. I helped with those experiments for the paper... the data from CITE-seq (their technology) looks much like flow cytometry data, and population identification is comparable. But the technology provides much, much more, since it gives transcriptome information as well, thereby providing rich characterization of cells. Some great projects are coming up using this technology. Nature Methods, July 2017 (click on the image to jump to the paper):

I think these technologies are where the future of cytometry is headed. I have dubbed these - and similar platforms - IMCPs (Integrated Molecular Cytometry Platforms), which I hope will catch on, tell your friends!

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