1/12.Excited to share our team's latest work and the first @xaira_thera preprint! Here, we introduce FiCS Perturb-seq, an industrialized platform for generating scaleable, high-quality perturbation data. 📄 Read the preprint:

RT @cziscience: New to virtual cells? @freethink’s primer explains these AI-powered, in silico models—why they’re game-changers for biology….

What do you want to know?.

In the wake of all the Bio x AI updates, I'm also here to spread awareness of important cat-related research. Isparta et al analyzed 400+ cat videos and found that cats favor the leftward side (p < 0.001), allowing for fast response to stimuli. Paper:

RT @Zhiliang_Bai: We are looking for talented postdocs/visiting students to join our team @MITdeptofBE and @kochinstitute to innovate spati….

Such an exciting initiative. Congrats to all those involved! . Hope people put the X-Atlas/Orion dataset to use 💪.

12/12.This work is the result of an incredible collaboration between @Xaira_Thera and @ForesiteLabs! Many thanks to all the amazing co-authors (see preprint for full list): @Stanley_TH @jiangzhuzime @qtaznangel @awblocker and @inCiChu.

11/12.In conclusion, FiCS Perturb-seq is a robust platform for executing large-scale perturbation screens, enabling the creation of datasets like X-Atlas/Orion to accelerate causal foundation models in predictive biology. 📊 Download X-Atlas/Orion:

10/12.As part of this work, we’re releasing X-Atlas/Orion, the largest publicly available Perturb-seq atlas to date! It contains ~8 million cells deeply sequenced to >16k UMIs per cell from the two genome-wide FiCS Perturb-seq screens described above.

9/12.This approach moves beyond treating genetic perturbations as simple on vs off switches, enabling a more nuanced understanding of how gene dosage impacts cellular responses. Indeed, we observed that more sgRNA leads to stronger cellular responses in the same perturbations.

8/12.Excitingly, we find that sgRNA abundance can be used as a reliable proxy for gene knockdown efficiency. This enables the dissection of dose-dependent genetic effects with unprecedented precision.

7/12.Data generated using FiCS Perturb-seq is biologically meaningful. We show it validates physical protein-protein interactions and clusters perturbations into known biological complexes, such as ribosome biogenesis, protein synthesis, and Mediator complex.

6/12.Furthermore, FiCS Perturb-seq also delivers remarkably consistent data with significantly lower batch-to-batch variation compared to previous atlases, ensuring consistency and reliability across experiments. This means better data for more robust models!

5/12.A major bottleneck of perturbation data generation is the need to use fresh cells. Using FiCS Perturb-seq, we show that cryopreservation of fixed cells maintained RNA-seq quality for up to 140 days, decoupling cell dissociation from library preparation.

4/12.Using FiCS Perturb-seq, we performed two genome-wide screens in HCT116 and HEK293T cell lines. Both screens had better sensitivity compared to existing genetic and chemical perturbation atlases, detecting a greater number of genes and UMIs per cell.

3/12.To overcome these challenges, we developed FiCS Perturb-seq, a platform that integrates fixation, FACS enrichment, cryopreservation, superloading, and automation for scalable, high-quality Perturb-seq data generation.

2/12.This work was inspired by the need for large, high-quality perturbation datasets to train foundation models that understand causal relationships of biology. Current methods for generating perturbation data are limited by throughput and batch-to-batch variability.

RT @Xaira_Thera: Today, we're releasing the fuel for the next generation of AI in biology 🧬. X-Atlas/Orion is now the largest public genome….

Excited to share that we are emerging from stealth mode today! Stay tuned for updates as we use AI to transform the drug discovery process.

RT @simocristea: I am often asked by scientists across fields for advice & resources on how to get started with #scRNAseq analyses. I summa….

RT @RongFan8: Thanks @NatRevNeurol for highlighting our work. Kudos to @_atchen !!.