… afterthoughts of the Keystone Single Cell Biology Symposium.
Why do we need to study biology at a single cell resolution?
Besides the technological appeal (and generating big seemingly unprocessable data), we have to ask ourselves, why on earth do we need to study biology at the single cell level? As technology evolves, we need to study biology from a singular lens! Here are some (compelling) reasons:
- Smoothies are not always delicious 😉
- Heterogeneity is the nature of the multicellular organisms.
- The beginning of the life is driven by one (stem) cell, who subsequently gives rise to millions of shades of colors of our body.
How is the field of “Single Cell Biology” evolving from purely descriptive facts of heterogeneity, hypothesis generating to meaningful mechanistic insights?
As of where the sequencing technology stands today, we mostly generate snapshots of tissue heterogeneity data. Sure, at each time point, almost every tissue is “heterogeneous” – so what? The key of such analysis is not just “showing” every cell is different (it is “no brainer”,right?), but to ask whether it is possible to discern some meaning new information of life, and infer better treatments of a given disease?
The challenge is how to connect the “snapshots” into a “story”. Can we use the current snapshots of single-cell gene expression data to infer causation (mechanisms) of tissue development or disease progression? Here are some interesting ideas presented on symposium, reflecting the current thinking and the direction of the field is heading:
- RNA velocity analysis – a cool idea!
- Predicting the trajectory using a comprehensive collection of bioinformatics tools (Dyno)
- High-resolution temporal series sampling coupled with CRISPR-based “time recorder”.
- Spatial annotated single-cell transcriptome using MERFISH.
- Integrative analysis of single-cell temporal single cell transcriptome (scRNA-seq) and scATAT-seq data (new Seurat 3.0).
Exciting cool technological advances in the field of Single Cell Biology
I started to know that Switzerland is such a paradise for research! There are a couple top tier talks during the meeting. One of the most “insane” ones is a new technology to “repetitive sampling of RNA from one cell over time”. As one said, “… after we collected the cell for single-cell sequencing analysis, the cells were pretty ‘dead’. :)”
Yes, indeed, they have become single molecules after lysing… The biggest challenge in the field is: how can we sample the cell transcriptome overtime without permanently destroy the cell? Some new data presented at the meeting is showing promise. By literally “sucking” a little cytoplasm repetitively, researchers now can look through the temporal changes of the same cell over time! Although it is still at its early stage, the quality of the RNA sampled is analyzable. Who told us we can not land on the moon? 😉
Unanswered the challenges
During the meeting, researchers also presented sophisticated time-lapse imaging data, from a single cell to 3D organoid, then to the whole brain. As cool as you can imagine, I feel there are limited advances in the imaging realm. Although the imaging speed and resolution have been improved tremendously, beyond the morphology and identification of the novel features, the molecular information associated with observed cell/tissue feature remains lacking. There still a big gap from so-called “cataloging” to molecular insights. While “Historia Naturalis” is how all biologist started, the modern cancer biologist, at least, desires actionable mechanisms. How can time-lapse imaging be “molecular”? Maybe our effort in using biosensors, coupled with fast FLIM imaging, is one of such “small” steps towards filling the “gap”. 🙂