Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles

Nicholas W Hughes; Yuanhao Qu; Jiaqi Zhang; Weijing Tang; Justin Pierce; Chengkun Wang; Aditi Agrawal; Maurizio Morri; Norma Neff; Monte M Winslow; Mengdi Wang; Le Cong

doi:10.1016/j.molcel.2022.06.001

Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles

Mol Cell. 2022 Aug 18;82(16):3103-3118.e8. doi: 10.1016/j.molcel.2022.06.001. Epub 2022 Jun 24.

Authors

Affiliations

¹ Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Wu Tsai Neuroscience Institute, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
² Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
³ Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁴ Chan Zuckerberg Biohub, Stanford, CA 94305, USA.
⁵ Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA; Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08544, USA. Electronic address: mengdiw@princeton.edu.
⁶ Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Wu Tsai Neuroscience Institute, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: congle@stanford.edu.

Abstract

The development of CRISPR-based barcoding methods creates an exciting opportunity to understand cellular phylogenies. We present a compact, tunable, high-capacity Cas12a barcoding system called dual acting inverted site array (DAISY). We combined high-throughput screening and machine learning to predict and optimize the 60-bp DAISY barcode sequences. After optimization, top-performing barcodes had ∼10-fold increased capacity relative to the best random-screened designs and performed reliably across diverse cell types. DAISY barcode arrays generated ∼12 bits of entropy and ∼66,000 unique barcodes. Thus, DAISY barcodes-at a fraction of the size of Cas9 barcodes-achieved high-capacity barcoding. We coupled DAISY barcoding with single-cell RNA-seq to recover lineages and gene expression profiles from ∼47,000 human melanoma cells. A single DAISY barcode recovered up to ∼700 lineages from one parental cell. This analysis revealed heritable single-cell gene expression and potential epigenetic modulation of memory gene transcription. Overall, Cas12a DAISY barcoding is an efficient tool for investigating cell-state dynamics.

Keywords: CRISPR barcoding; Cas12a; PRC2; high throughput screening; lineage tracking; machine learning; melanoma; online learning optimization; single cell genomics; transcriptional memory.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

CRISPR-Cas Systems*
Cell Lineage / genetics
DNA Barcoding, Taxonomic* / methods
Humans
Machine Learning
Phylogeny

Abstract

Publication types

MeSH terms

Grants and funding