Zhifei Luo

Scientist in the Greenleaf Lab @Stanford | Ph.D. @USC Fight On | Discover gene regulation mechanisms by developing new methods | Hope for aging intervention

[Chongyuan Luo @chongyuanluo]

What distinguishes successful from failed reprogramming to iPSC? In this new preprint, part of our @IGVFConsortium efforts, we used single cell methylation, Hi-C, RNA and ATAC, and spatial RNA to answer this question https://t.co/dKtCnB91sl

[Neal Amin @neal_amin]

Today, Sculpta releases the first demonstration of bioorthogonal barcoding ("bobcoding"), the process by which we:

1) covalently attach oligonucleotide barcodes to RNA at multiple internal positions with two-step chemistry, and 2) perform a multiplexed reverse transcription reaction to generate barcoded cDNA libraries with >99% barcoding accuracy.

To the best of our knowledge, it is the first time ever that either of these steps have ever been performed by anyone. No one has ever broken the barrier of multiplexing prior to any enzymatic step in library preparation, driving simplicity and higher data quality. Instead of other methods that add 0 or 1 barcode, we add one barcode every ~300bp, adding redundancy and fidelity to RNA measurements.

In our first application described in our pre-print below, we used bobcodes to create the first RNA isoform-resolved drug screening platform. It beats Novartis' DRUG-seq platform by:

- generating full length transcript capture of RNA isoforms, instead of just 3' end counting
- capturing 25-fold more RNA splicing events
- reducing barcode swapping by 10-fold
- using 18 fewer PCR cycles (~250,000 less amplification)
- reducing sample-to-sample variability in gene expression measurements
- eliminating costly and cumbersome library fragmentation/tagmentation steps completely
- reducing workflow complexity and number of steps
- reducing overall protocol duration by ~25%

Though our chemical barcoding method improved transcriptomic data quality while also being simpler and faster than all existing methods, the implications of this work go well beyond this particular use case:

The gate to greater applications of AI in transcriptomics is not a lack of compute. Nor is it a lack of data volume. The elephant in the room has always been our limited ability to faithfully and accurately measure cellular RNAs.

Sculpta now has line of sight to build what we call the first ground truth transcriptomics platform [for single cell, spatial and more] for the future of biological research, drug development and AI-enabled discoveries for the betterment of human health and longevity.

[PS Until the kind folks at bioRxiv get through the apparent backlog of AI slop submissions, Sculpta will host the pre-print PDF on our website. You can sign up for product offerings and other updates at this link too]

https://t.co/QPApERi7RT

[Anshul Kundaje @anshulkundaje]

This is total bullshit hype. I'm going to write up a more detailed piece on what this actually is vs. what it's being portrayed to be. But briefly ... 1/

[Zhifei Luo @LuoZhifei]

Same budget. More biology. no waiting for the core to free up. Just vortex and go.

[Zephyrus @ultraenhancer]

Exciting! I'm going to do PIP-Multiome.

[Zhifei Luo @LuoZhifei]

Not just cheaper -- high quality data. We get 40,000+ unique fragments per cell. Most competing methods report 352–9,258 (Table 1, PMID: 40419657).

[Zhifei Luo @LuoZhifei]

The integration of AI with single-cell genomics holds great promise, but progress remains constrained by the high cost and difficulty for data generation. Our new method makes single-cell ATAC-seq and multiomic profiling more practical and accessible.
https://t.co/Gi7RZpV19H

[Zhifei Luo @LuoZhifei]

Most methods are designed for ultra-high throughput—typically 10,000 or more cells—while many labs need smaller experiments across multiple conditions. Our method flexibly scales from 2,000 to 100,000 cells.

[William J. Greenleaf @WJGreenleaf]

Our preprint describing the adaptation of the PIP-seq platform for scalable, inexpensive multiomics is now live on BioRxiv! Congrats to Zhifei Luo (@LuoZhifei)! https://t.co/jIkXOd4spn