Ann Huang Profile
Ann Huang

@_annhuang

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i like science and cats. bioinformatics @xaira_thera

Joined September 2020
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@_annhuang
Ann Huang
15 days
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:
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@_annhuang
Ann Huang
4 days
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:
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@_annhuang
Ann Huang
6 days
RT @Zhiliang_Bai: We are looking for talented postdocs/visiting students to join our team @MITdeptofBE and @kochinstitute to innovate spati….
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@_annhuang
Ann Huang
8 days
Such an exciting initiative. Congrats to all those involved! . Hope people put the X-Atlas/Orion dataset to use 💪.
@arcinstitute
Arc Institute
9 days
Register today for the Virtual Cell Challenge and use AI to solve one of biology’s most complex problems. Announced in @CellCellPress, the competition is hosted by Arc Institute and sponsored by @nvidia, @10xGenomics, and @UltimaGenomics.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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:
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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!
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
15 days
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.
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@_annhuang
Ann Huang
18 days
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….
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@_annhuang
Ann Huang
1 year
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.
@Xaira_Thera
XairaTherapeutics
1 year
We’re re-engineering drug discovery & development through the end-to-end application of emerging AI technologies. Incubated by ARCH Venture Partners & @ForesiteLabs & led by founding CEO Marc Tessier-Lavigne:
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@_annhuang
Ann Huang
3 years
RT @simocristea: I am often asked by scientists across fields for advice & resources on how to get started with #scRNAseq analyses. I summa….
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@_annhuang
Ann Huang
3 years
RT @RongFan8: Thanks @NatRevNeurol for highlighting our work. Kudos to @_atchen !!.
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@_annhuang
Ann Huang
3 years
Returned to New Haven to celebrate the conclusion of my PhD! Thanks to all my family, friends, and mentors for their help throughout the years. We did it 🎉 #PhDone
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