Back

SCOPE: Localizing fate-decision states and their regulatory drivers in single-cell differentiation

Zhao, Y.; Finkbeiner, C.; Setty, M.; Lin, K.

2026-04-09 cell biology
10.64898/2026.04.07.717037 bioRxiv
Show abstract

Identifying the precise transcriptomic states at which cells commit to a lineage (branchpoints) and the temporal lag in which chromatin accessibility foreshadows gene expression (epigenetic priming) remain fundamental challenges in developmental biology. While current methods for single-cell sequencing data effectively capture developmental flow, they often lack a principled mechanism for delineating the discrete boundaries, a crucial aspect required to map the molecular logic of lineage commitment. We present SCOPE (Semi-supervised Conformal Prediction), a framework that transforms high-dimensional single-cell measurements into rigorous, discrete prediction sets of all plausible future fates. By formalizing fate uncertainty via conformal inference, SCOPE localizes the precise biological windows during which multipotent progenitors specify their fate. In multi-omic data, SCOPE uncovers epigenetic priming and identifies its driving transcription factors by detecting regimes where chromatin-derived prediction sets resolve toward terminal fates significantly before their transcriptomic counterparts. We apply SCOPE across simulations, lineage-traced mouse hematopoiesis, multiple human hematopoietic datasets, and human retinogenesis to demonstrate its broad applicability and ability to recapitulate known fate specification drivers. Ultimately, SCOPE provides a statistically grounded foundation for localizing fate decisions across biological replicates and modalities, offering a robust tool for identifying the onset of lineage specification in complex developmental systems.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Cell Systems
167 papers in training set
Top 0.2%
22.4%
2
Nature Methods
336 papers in training set
Top 1.0%
10.0%
3
Nature
575 papers in training set
Top 4%
6.3%
4
Nature Cell Biology
99 papers in training set
Top 0.6%
6.3%
5
Genome Biology
555 papers in training set
Top 1%
4.8%
6
Developmental Cell
168 papers in training set
Top 4%
4.3%
50% of probability mass above
7
Nature Communications
4913 papers in training set
Top 37%
3.9%
8
Science
429 papers in training set
Top 9%
3.6%
9
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 20%
3.6%
10
Nature Biotechnology
147 papers in training set
Top 3%
3.6%
11
Nature Genetics
240 papers in training set
Top 3%
2.1%
12
Cell Reports
1338 papers in training set
Top 21%
2.1%
13
Journal of Cell Biology
333 papers in training set
Top 2%
2.1%
14
Nature Machine Intelligence
61 papers in training set
Top 2%
1.9%
15
eLife
5422 papers in training set
Top 45%
1.5%
16
PLOS ONE
4510 papers in training set
Top 62%
1.1%
17
Science Advances
1098 papers in training set
Top 25%
0.9%
18
Nucleic Acids Research
1128 papers in training set
Top 15%
0.9%
19
PLOS Computational Biology
1633 papers in training set
Top 22%
0.9%
20
Nature Medicine
117 papers in training set
Top 4%
0.8%
21
Cell
370 papers in training set
Top 16%
0.8%
22
Advanced Science
249 papers in training set
Top 19%
0.7%
23
Nature Neuroscience
216 papers in training set
Top 6%
0.7%
24
iScience
1063 papers in training set
Top 35%
0.7%
25
Scientific Reports
3102 papers in training set
Top 77%
0.7%
26
Development
440 papers in training set
Top 4%
0.7%
27
Molecular Cell
308 papers in training set
Top 11%
0.6%
28
Cell Genomics
162 papers in training set
Top 8%
0.6%