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Ranking reprogramming factors for cell differentiation

Nature Methods volume 19, pages 812–822 (2022)Cite this article

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Abstract

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50–60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

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Fig. 1: Identifying transcription factors that reprogram starting cells to target cell types.
Fig. 2: Selection of genomic regions affects traditional DNA sequence-based methods for identification of transcription factors from chromatin accessibility.
Fig. 3: Use of histone mark and EP300 annotation does not significantly affect transcription factor recovery in liver cells.
Fig. 4: Complex chromatin methods are top performers for transcription factor recovery and significance ranking.

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Data availability

Normalized area under rank recall curve values for all methods are available in Supplementary Tables 2–6 and for epigenomic marks of liver cells in Extended Source Data Fig. 3. Ranks of each reprogramming factor for all methods are available in Supplementary Table 8. The consensus mouse transcription factor motif database derived from the mouse HOCOMOCOv11 database50, shared mouse enhancer sequences, and a list of mouse transcription factors are available at: https://cgs.csail.mit.edu/ReprogrammingRecovery/. Publicly available ATAC-seq and RNA-seq samples were downloaded as fastqs from Nucleotide Read Archive (Supplementary Table 1) and processed as described in the sections on ATAC-seq processing and RNA-seq processing. Uniformly processed gene count and peak files are also available at https://cgs.csail.mit.edu/ReprogrammingRecovery/. Data collection software used were conda/bioconda (v.4.9.0), bedtools (v.2.29.2), Trimgalore) (v.21032019), cutadapt (v.0.6.2), samtools (v.1.7), bwa (v.0.7.17), MACS2 (v.2.2.7.1), FASTQC (v.0.11.8), STAR (v.2.5.2b), RSEM (v.1.3.0), R (v.3.6.1), python (v.3.6.9), DeepAccess (v.0.0.1), EBseq (v.1.2.0), CellNet (v.0.1.0), GarNet (v.0.5.0), HOMER (v.4.9.1), AME/DREME/TomTom (v.5.0.5), KMAC (GEM v.3.4), diffTF (v.1.7.1), PWMScan (v.1.1.1), HOCOMOCO (v.11), GENCODE (v.m24) and mouse genome (mm10), and are cited in Supplementary Table 17. Source data are provided with this paper.

Code availability

The custom script for performing motif discovery with AME, DREME, HOMER and KMAC is available at: https://cgs.csail.mit.edu/ReprogrammingRecovery/.

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Acknowledgements

We thank members of the Gifford and Wichterle laboratories for helpful discussions. We gratefully acknowledge funding from 1RO1HG008363 (D.G.), 1R01HG008754 (D.G.), 1R01NS109217 (D.G. and H.W.), R01NS116141 (H.W.), NINDS Postdoctoral NRSA Fellowship (F32NS105372) (T.P.), Brain Initiative K99 (1K99NS121136) (T.P.) and National Science Foundation Graduate Research Fellowship (1122374) (J.H.).

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Authors and Affiliations

  1. Computational and Systems Biology, MIT, Cambridge, MA, USA

    Jennifer Hammelman & David Gifford

  2. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA

    Jennifer Hammelman & David Gifford

  3. Departments of Pathology and Cell Biology, Neuroscience, Rehabilitation and Regenerative Medicine (in Neurology), Columbia University Irving Medical Center, New York, NY, USA

    Tulsi Patel, Michael Closser & Hynek Wichterle

  4. Center for Motor Neuron Biology and Disease, Columbia University Irving Medical Center, New York, NY, USA

    Tulsi Patel, Michael Closser & Hynek Wichterle

  5. Columbia Stem Cell Initiative, Columbia University Irving Medical Center, New York, NY, USA

    Tulsi Patel, Michael Closser & Hynek Wichterle

  6. Department of Biological Engineering, MIT, Cambridge, MA, USA

    David Gifford

  7. Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA

    David Gifford

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Contributions

Data curation and visualization were carried out by J.H. The original draft was written by J.H. Reviewing and editing of the draft were carried out by J.H., T.P., M.C., H.W. and D.G. D.G. and H.W. supervised the work. Funding was acquired by J.H., T.P., H.W. and D.G.

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Correspondence to David Gifford.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Madhura Mukhopadhyay was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 A consensus database of 107 transcription factor motifs.

a, HOCOMOCO v11 mouse transcription factor core motif database is used as input. Motif PWM similarity to the HOCOMOCO database is computed using Tomtom. b, For each pair of motifs, Pearson correlation between Tomtom scores is computed, resulting in a symmetric correlation matrix. Affinity propagation clustering is applied to the correlation matrix, resulting in 107 clusters of transcription factor motifs with one motif being selected as the representative motif of the cluster. c, Cluster representing OCT/SOX heterodimer-like motifs with SOX2 motif selected as the representative. d, Cluster representing LIM-like motifs with LHX3 motif selected as the representative.

Source data

Extended Data Fig. 2 Comparing input features and methods for transcription factor recovery from chromatin accessibility data.

a, Reprogramming recovery effected estimated by linear models for decision axes in input to chromatin models for AURC of top 100 ranked factor motifs, excluding predicting stem cell reprogramming factors to estimate effect of use of fibroblast or stem cell as source cell type is selection of cell type-specific regions, or selection of top regions without eliminating regions that are accessible in the source cell type. b, Cell type AURC for top 100 ranked factor motifs stratified by decision axis and marginalized over other axes. Box plots show median and quartile values. Whiskers extend to represent the rest of the data distribution with the exception of outliers that are defined as values greater than 1.5 times the inter-quartile range and are plotted as individual points.

Source data

Extended Data Fig. 3 Comparing chromatin accessibility overlapping histone mark and EP300 epigenomic data for transcription factor recovery.

AURC for top 100 ranked factor motifs in liver using overlaps between chromatin accessibility (ATAC-seq) and overlap of chromatin accessibility with H3K27ac, EP300, H3K4me1, H3K4me3, and 3 enhancer markers (EP300, H3K27ac, and H3K4me1) per method identifies for DREME, HOMER, and KMAC worst performance using ATAC + H3K4me3 which is correlated with promoter activity, and for all methods we see similar performance levels with ATAC, ATAC + H3K27ac, and ATAC + H3K4me1 which mark enhancers.

Source data

Extended Data Fig. 4 Fibroblast and stem cell as starting cell type comparing each method.

Chromatin methods use optimal input features for each background a, Normalized area under the rank recall curve for top 10 ranked motifs averaged over cell types, b, scatter plot of normalized area under the recall curve for fibroblast (x-axis) and stem cell (y-axis) each dot represents the normalized area under the rank recall curve for top 10 ranked motifs for one cell type and one method where color represents the method, c, scatter plot of normalized area under the recall curve for fibroblast (x-axis) and stem cell (y-axis) each dot represents the normalized area under the rank recall curve for top 100 ranked motifs for one cell type and one method where color represents the method.

Source data

Extended Data Fig. 5 GarNet distance thresholds do not majorly impact performance for transcription factor recovery.

GarNet fraction of reprogramming factors over eight target cell types recovered by 2 kb, 10 kb, and 100 kb thresholds for maximum distance between transcription factor binding site and gene transcription factor start site.

Source data

Extended Data Fig. 6 Deciding input and methods for ranking reprogramming transcription factors.

Decision chart for performing optimal reprogramming factor recovery given chromatin accessibility data for a desired target reprogramming factor cell type.

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Hammelman, J., Patel, T., Closser, M. et al. Ranking reprogramming factors for cell differentiation. Nat Methods 19, 812–822 (2022). https://doi.org/10.1038/s41592-022-01522-2

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