Reading the Light-seq paper in depth, and I'm blown away by the incredible synthesis of prior DNA technologies from this group -- every component is both ingenious and entirely necessary

Also loving the growing adoption of the term 'ex situ sequencing' 😎
nature.com/articles/s4159…

Light-Seq technology developed at the Wyss Institute allows for the first time the sequencing of the full transcriptomes of cells selected with light in tissues under the microscope. wyss.harvard.edu/news/turning-t…

Josie Kishi Emma West

One more BIG thanks to Josie Kishi, Sinem Saka, Ninning Liu, and Peng Yin who laid the foundation for this work over many years. So awesome working with you at Wyss Institute and Harvard Medical School!

This is absolutely wild work! Using light to print DNA barcodes which then act as GPS tags that can be read in situ 🤯 Taking spatial biology to the next level! Can't wait to see what's next for this tech! 👏 Josie Kishi Emma West Sinem Saka and team!

Amazing to be part of such an inspiring team of spatial explorers👩‍🚀🧬Emma West Josie Kishi
Check out how we can use light to target specific cell populations for sequencing while preserving spatial information!

nature.com/articles/s4159…

What if you could image 🔬cells and then choose which ones to sequence 🧬?

What if you could go back and image 🔬cells that you previously sequenced 🧬?

Now you can 🔬 then 🧬 then 🔬 cells again with Light-Seq!

nature.com/articles/s4159…

Let’s dig in 🧵.

Wow so cool— I’ve been eagerly awaiting the Light-seq publication!

Check out an awesome spatial seq technology (nature.com/articles/s4159…) by the amazing Emma West + Josie Kishi!

One more BIG thanks to Josie Kishi, Sinem Saka, Ninning Liu, and Peng Yin who laid the foundation for this work over many years. So awesome working with you at Wyss Institute and Harvard Medical School!

Check out links to raw data, GitHub, and user-friendly protocols on protocols.io, all linked on lightseq.io!

Thanks to NIH, HHMI NEWS, Wyss Institute, CZI Science at Chan Zuckerberg Initiative for funding this project!

Light-Seq can close the loop: after sequencing, we ordered an antibody to detect the top TH+ marker (CARTPT) and applied it to the ❗️SAME CELLS❗️ that we sequenced to assess protein expression. The image on the right here was taken >2 weeks after sequencing this exact same cell!

Precise barcoding ➡️ high-confidence sequencing. We detected an average of 7,800 UMIs per TH+ cell. Light-Seq is sensitive enough to find markers with a range of expression from just 4-8 cells/sample. Here’s two of the top markers detected by RNA-FISH for my 👁️retina👁️ people 🤔:

Light-Seq detected 18,000 ‼️ genes across the TH+ and TH- cells and discovered dozens of RNA markers significantly enriched in the rare TH+ cells. We validated the top markers by RNA 🐟 in new animals, confirming their enriched expression.

Light-Seq enables sequencing of even rare, disjoint cell populations in fixed tissues. We barcoded each TH+ cell by following the contour of the cell bodies directly (🔴, below). We also barcoded TH- cells (🟢, below) for comparison, to find RNA markers specific to TH+ cells

We applied Light-Seq to study a VERY rare neuron that has been notoriously difficult: dopaminergic amacrine cells. These are marked by tyrosine hydroxlase (TH) protein, and are 1 in 20,000 cells in the mouse retina. Each section with 4-8 cells, and we analyzed just 5⃣ sections

After sequencing, we returned to the same cells and stained for proteins, genomic DNA (DAPI), and cell membranes (WGA). High-res imaging after full-transcriptome sequencing, in the same fixed cells/tissue section! 🧬🔬

Light-Seq detected >24,000 genes ‼️ and recovered thousands of layer-specific biomarkers, consistent with existing single-cell RNA sequencing data.

Quick S/O to Paula Montero-Llopis + MicRoN Core for the amazing microscopy resources!

Ok, what can you do with Light-Seq? We first benchmarked against scRNA sequencing data. We took 4⃣ serial tissue sections (18 um) from the mouse retina and barcoded three main cellular layers with well-studied cell compositions. Each layer had between 50 and 2000 cells.