Compressed Perturb-seq enables highly efficient genetic screens

Using an experimental and computational framework inspired by compressed sensing, we greatly reduced the number of measurements needed to run Perturb-seq. Our compressed Perturb-seq strategy relies on collecting measurements comprising random linear combinations of genetic perturbations, followed by deconvolving the perturbation effects on the transcriptome using sparsity-exploiting algorithms.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

209,00 € per year

only 17,42 € per issue

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Learn about institutional subscriptions

Read our FAQs

Contact customer support

Fig. 1: Compressed Perturb-seq.

References

Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016). This was one of the initial papers that developed Perturb-seq.

Article
CAS
PubMed
PubMed Central

Google Scholar

Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559–2575 (2022). This paper represents the largest Perturb-seq screen conducted to date.

Article
CAS
PubMed
PubMed Central

Google Scholar

Candes, E. J. & Wakin, M. B. An introduction to compressive sampling. IEEE Signal Process. Mag. 25, 21–30 (2008). This review article presents an overview of compressed sensing.

Article

Google Scholar

Cleary, B. & Regev, A. The necessity and power of random, under-sampled experiments in biology. Preprint at https://doi.org/10.48550/arXiv.2012.12961 (2020). This review article presents a general framework for efficient data collection using random experiments.

Sharan, V. et al. Compressed factorization: fast and accurate low-rank factorization of compressively-sensed data. Proc. 36th Int. Conf. Machine Learning 5690–5700 (2019). This paper provides the theoretical basis for the computational method introduced in our paper.

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Yao, D. et al. Scalable genetic screening for regulatory circuits using compressed Perturb-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01964-9 (2023).