arxiv: 2604.16642 · v2 · submitted 2026-04-17 · 🧬 q-bio.QM · q-bio.CB· q-bio.GN· stat.AP

Recognition: 2 theorem links

· Lean Theorem

Geometric coherence of single-cell CRISPR perturbations reveals regulatory architecture and predicts cellular stress

Prashant C. Raju

Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:40 UTC · model grok-4.3

classification 🧬 q-bio.QM q-bio.CBq-bio.GNstat.AP

keywords single-cell CRISPRperturbation coherencegeometric stabilityregulatory architecturecellular stressCRISPR screensexpression shift vectors

0 comments

The pith

A geometric coherence metric for CRISPR perturbations correlates with effect size yet independently flags cellular stress and regulatory type.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines a stability score, Shesha, as the average cosine similarity of each perturbed cell's expression change to the mean change for that perturbation. Across thousands of perturbations in multiple single-cell CRISPR datasets, this score tracks closely with how far cells move on average, but the two measures split for certain genes. Pleiotropic regulators produce large average shifts that scatter cells in many directions, while lineage-specific factors produce shifts that stay aligned. After holding magnitude fixed, lower stability still associates with higher expression of stress markers such as the chaperone HSPA5. The same magnitude-stability link appears when the data are passed through a foundation-model embedding, indicating the pattern is not an artifact of the original linear space.

Core claim

Perturbation stability, quantified as the mean cosine similarity between individual cell shift vectors and the mean perturbation direction, correlates strongly with effect magnitude across CRISPRa, CRISPRi, and pooled screens. When the two metrics diverge, pleiotropic master regulators such as CEBPA and GATA1 generate large yet directionally incoherent responses, whereas factors such as KLF1 produce tightly coordinated ones. After statistical control for magnitude, lower stability independently associates with elevated chaperone activation, and the high-stability high-stress quadrant is depleted; the relationship survives in scGPT embeddings.

What carries the argument

Shesha, the geometric stability metric defined as the mean cosine similarity of each cell's expression shift vector to the average shift vector for that perturbation; it quantifies directional coherence of the cellular response.

If this is right

Pleiotropic regulators incur a geometric tax by driving large but scattered responses, while lineage-specific regulators produce aligned ones.
Geometric instability remains linked to chaperone activation even after magnitude is accounted for.
The magnitude-stability relationship holds inside learned embeddings, showing the pattern belongs to biological state space.
Stability supplies a second axis for hit prioritization in screens and for quality control in engineered cell populations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same coherence measure could be computed on non-CRISPR perturbations to test whether the magnitude-stability link is general.
In silico perturbation models could be scored not only on predicted average shift but also on how coherently their simulated cells move.
Absence of a high-stability high-stress region may indicate that coherent large shifts are buffered against stress responses.

Load-bearing premise

That the average shift vector across cells is a biologically meaningful reference direction and that cosine similarity on expression changes measures genuine coherence rather than technical noise or normalization artifacts.

What would settle it

A fresh single-cell CRISPR dataset in which the partial correlation between stability and stress-marker expression (after controlling for magnitude) is statistically indistinguishable from zero, or in which the rank correlation between magnitude and stability drops below 0.5.

Figures

Figures reproduced from arXiv: 2604.16642 by Prashant C. Raju.

**Figure 1.** Figure 1: The Geometric Tax: linear metrics obscure biological stability. (a) Standard dimensionality reduction projects high-dimensional cell states onto a flat plane (Linear Illusion, inset), where two populations (blue, red) appear to overlap, suggesting similar phenotypes. Mapping these populations onto the underlying biological manifold (Manifold Reality) reveals distinct stability properties invisible to linea… view at source ↗

**Figure 2.** Figure 2: Perturbation stability tracks effect magnitude across CRISPR modalities and cell types (a–e) Effect magnitude (Euclidean distance, 𝑥-axis) vs perturbation stability (𝑆𝑝 , cosine coherence, 𝑦-axis) for each of five datasets: (a) Norman 2019 CRISPRa in K562 (𝑛 = 236, 𝜌 = 0.953), (b) Adamson 2016 CRISPRi (𝑛 = 8, 𝜌 = 0.929), (c) Dixit 2016 CRISPRi in BMDCs (𝑛 = 153, 𝜌 = 0.746), (d) Papalexi 2021 pooled screen … view at source ↗

**Figure 3.** Figure 3: Geometric instability is independently associated with chaperone activation (a) Perturbation stability (𝑥-axis) vs HSPA5 (BiP) expression (𝑦-axis) in the Replogle 2022 CRISPRi dataset (𝑛 = 1,832). Red line: linear regression with 95% confidence interval (gray shading). Dotted lines: median splits defining quadrants. The high-stability/high-stress (HH) quadrant is depleted: 301 observed vs 458 expected unde… view at source ↗

**Figure 4.** Figure 4: The magnitude-stability relationship persists in nonlinear foundation model embeddings Effect magnitude (𝑥-axis) vs perturbation stability (𝑦-axis) computed in scGPT “Whole Human” embeddings for three datasets: (a) Norman 2019 (𝑛 = 236, scGPT 𝜌 = 0.935, PCA 𝜌 = 0.953), (b) Dixit 2016 (𝑛 = 153, scGPT 𝜌 = 0.712, PCA 𝜌 = 0.746), (c) Replogle 2022 (𝑛 = 1,832, scGPT 𝜌 = 0.851, PCA 𝜌 = 0.970). Dashed lines: line… view at source ↗

**Figure 5.** Figure 5: Combinatorial perturbations exhibit higher geometric stability than single-gene perturbations (a) Distribution of perturbation stability (𝑆𝑝 ) for single-gene (𝑛 = 105) vs combinatorial (𝑛 = 131) perturbations in the Norman 2019 CRISPRa dataset. Combinatorial perturbations show significantly higher stability (Mann-Whitney 𝑈 test). Boxes indicate interquartile range; internal line indicates median. (b) Magn… view at source ↗

**Figure 6.** Figure 6: Extended discordance analysis in the Replogle 2022 genome-scale CRISPRi screen. Effect magnitude (Euclidean, 𝑥-axis) versus Shesha stability (cosine, 𝑦-axis) for 𝑛 = 1,832 perturbations in Replogle et al. (2022) K562 cells. Dashed line shows linear fit. Points are categorized by biological function: Discordant (red): high magnitude but low stability relative to regression, including GATA1 (master regulato… view at source ↗

**Figure 7.** Figure 7: Magnitude-stability correlation is robust across distance metrics. Bar chart showing Spearman correlations with 95% bootstrap CIs (error bars) for three distance computation methods: Euclidean (standard 𝐿2 in PCA space), Whitened (Mahalanobis-scaled coordinates), and 𝑘-NN (local control centroids). All methods achieve strong correlations (𝜌 > 0.67) across all datasets. Whitening substantially improves the… view at source ↗

**Figure 8.** Figure 8: PCA dimensionality ablation. Magnitude-stability Spearman 𝜌 as a function of the number of principal components retained (10, 20, 30, 50, 100). Shaded regions indicate 95% bootstrap CIs (10,000 iterations). Norman 2019 shows stable correlations (𝜌 = 0.94–0.96) with overlapping CIs across all settings. Dixit 2016 shows modest improvement with more PCs (𝜌 = 0.67 to 0.79), suggesting higher-dimensional struct… view at source ↗

**Figure 9.** Figure 9: Random seed reproducibility. Magnitude-stability Spearman 𝜌 recomputed across 15 different random seeds ({3, 7, 9, 11, 12, 18, 103, 108, 320, 411, 724, 1754, 1991, 2222, 7258}) for Norman 2019 and Replogle 2022. All correlations are identical to machine precision (cross-seed 𝑟 = 1.000), confirming that stochastic elements in the preprocessing pipeline (PCA initialization) have no effect on the final result… view at source ↗

**Figure 10.** Figure 10: Leave-one-out influence analysis. Distribution of Δ𝜌 values when each perturbation is removed in turn. The LOO range is narrow for all datasets: removing any single perturbation changes the correlation by at most Δ𝜌 = 0.002 (Norman), Δ𝜌 = 0.014 (Dixit), or Δ𝜌 < 0.001 (Replogle). Most influential perturbations: BAK1 (most helpful, Norman), HES7 (most harmful, Norman), ELK1 (most helpful, Dixit), CREB1+E2F4… view at source ↗

**Figure 11.** Figure 11: Theoretical null model under isotropic Gaussian perturbations. Magnitude (𝑥-axis) versus stability (𝑦- axis) for 2,000 simulated perturbations (𝑑 = 50 dimensions, 𝜎 ∈ {0.5, 1.0, 2.0, 3.0}, 500 simulations per condition). Under the null model, stability is almost perfectly predicted by SNR (𝜌 = 0.999), with a partial correlation of 𝜌partial = 0.292 after controlling for SNR. The heterogeneity observed in r… view at source ↗

**Figure 12.** Figure 12: Stress marker correlations with geometric stability. Forest plot of Spearman correlations between perturbation stability (𝑆𝑝 ) and mean expression of four canonical stress response markers (DDIT3, ATF4, XBP1, HSPA5) across three datasets (Norman, Dixit, Replogle). Bars extend to 95% bootstrap CIs. Significant associations (𝑝 < 0.001) in bold. HSPA5 shows the most consistent negative association across da… view at source ↗

**Figure 13.** Figure 13: Per-perturbation concordance between PCA and scGPT stability estimates. Each panel shows PCAderived stability (𝑥-axis) versus scGPT-derived stability (𝑦-axis) for shared perturbations, with identity line (dashed). Point color indicates local perturbation density. Spearman 𝜌 of paired values annotated. High concordance confirms that stability rankings are preserved across linear and nonlinear embedding s… view at source ↗

**Figure 14.** Figure 14: Quadrant depletion analysis of stability versus stress. Perturbations split at median stability and median stress expression (dashed lines). Quadrant counts annotated. The high-stability/high-stress (HH) quadrant is systematically depleted across multiple stress markers and datasets (Fisher’s exact test; [PITH_FULL_IMAGE:figures/full_fig_p031_14.png] view at source ↗

**Figure 15.** Figure 15: Stress marker correlations by dataset and modality. Heatmap of Spearman correlations between geometric stability and four stress markers (DDIT3, ATF4, XBP1, HSPA5) across three datasets. Color scale: blue (positive) to red (negative), centered at zero. The heterogeneity across markers and datasets reflects differences in baseline stress levels, perturbation modality (CRISPRa versus CRISPRi), and the speci… view at source ↗

read the original abstract

Genome engineering has achieved remarkable sequence-level precision, yet predicting the transcriptomic state that a cell will occupy after perturbation remains an open problem. Single-cell CRISPR screens measure how far cells move from their unperturbed state, but this effect magnitude ignores a fundamental question: do the cells move together? Two perturbations with identical magnitude can produce qualitatively different outcomes if one drives cells coherently along a shared trajectory while the other scatters them across expression space. We introduce a geometric stability metric, Shesha, that quantifies the directional coherence of single-cell perturbation responses as the mean cosine similarity between individual cell shift vectors and the mean perturbation direction. Across five CRISPR datasets (2,200+ perturbations spanning CRISPRa, CRISPRi, and pooled screens), stability correlates strongly with effect magnitude (Spearman $\rho=0.75-0.97$), with a calibrated cross-dataset correlation of 0.97. Crucially, discordant cases where the two metrics decouple expose regulatory architecture: pleiotropic master regulators such as CEBPA and GATA1 pay a "geometric tax," producing large but incoherent shifts, while lineage-specific factors such as KLF1 produce tightly coordinated responses. After controlling for magnitude, geometric instability is independently associated with elevated chaperone activation (HSPA5/BiP; $\rho_{partial}=-0.34$ and $-0.21$ across datasets), and the high-stability/high-stress quadrant is systematically depleted. The magnitude-stability relationship persists in scGPT foundation model embeddings, confirming it is a property of biological state space rather than linear projection. Perturbation stability provides a complementary axis for hit prioritization in screens, phenotypic quality control in cell manufacturing, and evaluation of in silico perturbation predictions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Shesha adds a coherence axis to perturbation screens that decouples from magnitude and flags stress associations, but its value hinges on whether the mean-direction cosine survives normalization choices.

read the letter

The main point is that the authors define Shesha as average cosine similarity of per-cell shift vectors to the mean perturbation direction, then show across five CRISPR datasets that this stability tracks effect size but also splits from it in informative ways. Master regulators produce large yet scattered responses while lineage factors stay coherent, and after controlling for magnitude the lower-stability cases link to elevated HSPA5. The pattern holds in scGPT embeddings, which is a useful check that the signal is not just linear projection artifact. That multi-dataset consistency and the partial-correlation results are the concrete advance; the metric itself is simple geometry applied systematically where it had not been before. The soft spot is exactly what the stress test flags: shift vectors are built on log-normalized counts, so library-size correction, log base, and any centering directly shape both the individual directions and the mean reference. With modest cells per perturbation the mean is noisy, and cosine values can partly capture how technical variation aligns with that noise rather than true biological coordination. The abstract reports the Spearman ranges and partials but gives no vector-construction or batch-correction steps, so the claim that this is intrinsic to biological state space rests on unshown robustness checks. The correlations are real in the reported numbers, yet their size (partial r around -0.3) leaves room for preprocessing to contribute. This is for labs running or re-analyzing single-cell perturbation screens who need an extra axis for hit ranking or quality control; it will not rewrite the field but supplies a practical second metric. The thinking is direct and the data engagement looks honest, so it deserves peer review to test the preprocessing sensitivity and confirm the vector details.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces Shesha, a geometric stability metric defined as the mean cosine similarity between individual cell perturbation shift vectors and the mean perturbation direction. Across five CRISPR datasets, it reports strong Spearman correlations (ρ=0.75-0.97) between Shesha and effect magnitude, identifies discordant cases that reveal regulatory architecture (e.g., pleiotropic factors like CEBPA incur a geometric tax while lineage-specific factors like KLF1 produce coherent responses), and shows that geometric instability is independently associated with elevated stress (partial ρ with HSPA5 of -0.34 and -0.21) after controlling for magnitude. The magnitude-stability relationship persists in scGPT embeddings, supporting the claim that it reflects properties of biological state space rather than linear projection artifacts.

Significance. If the metric proves robust, it supplies a useful orthogonal axis to effect magnitude for interpreting single-cell CRISPR screens, enabling better distinction between coherent and scattered responses and linking instability to chaperone activation. The cross-dataset replication and persistence in foundation-model embeddings strengthen the case for biological relevance, with potential applications in hit prioritization, cell-manufacturing QC, and benchmarking in silico perturbation models.

major comments (1)

[Methods] Methods section (Shesha definition and vector construction): The computation of per-cell shift vectors is not described with respect to library-size normalization, log-transform base, centering, or batch correction, nor is the exact procedure for obtaining the mean perturbation direction specified. Because Shesha is the average cosine similarity to this mean and the mean itself is estimated from modest numbers of cells per perturbation, the reported Spearman range (0.75-0.97) and partial correlations with HSPA5 could partly arise from shared sensitivity to the same preprocessing pipeline rather than intrinsic coherence. This detail is load-bearing for the central claim that the relationship is a property of biological state space.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights an important area for improving methodological transparency. We address the single major comment below and will revise the manuscript to incorporate the requested details.

read point-by-point responses

Referee: [Methods] Methods section (Shesha definition and vector construction): The computation of per-cell shift vectors is not described with respect to library-size normalization, log-transform base, centering, or batch correction, nor is the exact procedure for obtaining the mean perturbation direction specified. Because Shesha is the average cosine similarity to this mean and the mean itself is estimated from modest numbers of cells per perturbation, the reported Spearman range (0.75-0.97) and partial correlations with HSPA5 could partly arise from shared sensitivity to the same preprocessing pipeline rather than intrinsic coherence. This detail is load-bearing for the central claim that the relationship is a property of biological state space.

Authors: We agree that the Methods section must be expanded for full reproducibility. In the revised manuscript we will add an explicit subsection on shift-vector construction: library-size normalization to 10,000 counts per cell, log1p transformation (natural base), centering by subtraction of the per-gene mean across control cells within each dataset, and no additional batch correction beyond the preprocessing supplied by the original dataset authors. The mean perturbation direction is defined as the arithmetic average of the individual cell shift vectors for that perturbation. To address the concern that correlations could be preprocessing artifacts, we note that the magnitude-stability relationship is reproduced in scGPT embeddings, which are generated by a transformer model operating on tokenized counts and do not rely on the same linear normalization and centering steps. We will include pseudocode and a supplementary table enumerating the exact preprocessing choices for each of the five datasets. revision: yes

Circularity Check

0 steps flagged

No circularity: metric definition and empirical correlations are independent

full rationale

The paper introduces Shesha explicitly as a new geometric metric (mean cosine similarity of per-cell shift vectors to the empirical mean perturbation direction) and then computes all downstream results—Spearman correlations with effect magnitude (ρ=0.75-0.97), partial correlations with HSPA5, quadrant depletion, and persistence in scGPT embeddings—as direct statistical summaries of the observed data vectors. No equation reduces the reported associations to the definition by algebraic identity, no parameter is fitted on a subset and then relabeled as a prediction, and no self-citation chain or uniqueness theorem is invoked to justify the metric or its relationships. The derivation chain therefore consists of definition followed by independent empirical measurement, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The metric rests on the assumption that expression shifts can be treated as vectors in a Euclidean space whose mean direction is biologically meaningful; no free parameters are introduced beyond standard statistical choices.

axioms (1)

domain assumption Expression changes can be represented as vectors whose cosine similarity to their mean is a valid measure of directional coherence.
Invoked in the definition of Shesha; standard in vector-space models of transcriptomics but not proven here.

invented entities (1)

Shesha metric no independent evidence
purpose: Quantify directional coherence of perturbation responses
Newly defined quantity; no independent evidence outside the paper's datasets.

pith-pipeline@v0.9.0 · 5624 in / 1438 out tokens · 30403 ms · 2026-05-13T07:40:10.271332+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
Shesha stability S_p = (1/n_p) Σ (d_i · d-bar) / (‖d_i‖ ‖d-bar‖)
IndisputableMonolith/Foundation/AlexanderDuality.lean alexander_duality_circle_linking unclear
magnitude-stability relationship persists in scGPT embeddings

Reference graph

Works this paper leans on

82 extracted references · 82 canonical work pages · 1 internal anchor

[1]

A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response

Britt Adamson, Thomas M. Norman, Marco Jost, Min Y. Cho, James K. Nuñez, Yuwen Chen, Jacqueline E. Villalta, Luke A. Gilbert, Max A. Horlbeck, Marco Y. Hein, Ryan A. Pak, Andrew N. Gray, Carol A. Gross, Atray Dixit, Oren Parnas, Aviv Regev, and Jonathan S. Weissman. “A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the ...

work page doi:10.1016/j.cell.2016.11.048 2016
[2]

Expression and regulation of C/EBP𝛼in normal myelopoiesis and in malignant transformation

Roberto Avellino and Ruud Delwel. “Expression and regulation of C/EBP𝛼in normal myelopoiesis and in malignant transformation”. In:Blood129.15 (2017), pp. 2083–2091.issn: 1528-0020.doi:10.1182/blood-2016-09-687822. 12

work page doi:10.1182/blood-2016-09-687822 2017
[3]

Potency assay development for cellular therapy products: an ISCT review of the requirements and experiences in the industry

Christopher A. Bravery, Jessica Carmen, Timothy Fong, Wanda Oprea, Karin H. Hoogendoorn, Juliana Woda, Scott R. Burger, Jon A. Rowley, Mark L. Bonyhadi, and Wouter Van’t Hof. “Potency assay development for cellular therapy products: an ISCT review of the requirements and experiences in the industry”. In:Cytotherapy15.1 (Jan. 2013), 9– 19.e9.issn: 1465-324...

work page doi:10.1016/j.jcyt.2012.10.008.url:http://dx.doi.org/10.1016/j.jcyt.2012 2013
[4]

Easy quantitative assessment of genome editing by sequence trace decomposition

Eva K. Brinkman, Tao Chen, Mario Amendola, and Bas van Steensel. “Easy quantitative assessment of genome editing by sequence trace decomposition”. In:Nucleic Acids Research42.22 (Oct. 2014), e168–e168.issn: 0305-1048. doi:10.1093/nar/gku936.url:http://dx.doi.org/10.1093/nar/gku936

work page doi:10.1093/nar/gku936.url:http://dx.doi.org/10.1093/nar/gku936 2014
[5]

ISG20L2, a Novel Vertebrate Nucleolar Exoribonuclease Involved in Ribosome Biogenesis

Yohann Couté, Karine Kindbeiter, Stéphane Belin, Régis Dieckmann, Laurent Duret, Laurent Bezin, Jean-Charles Sanchez, and Jean-Jacques Diaz. “ISG20L2, a Novel Vertebrate Nucleolar Exoribonuclease Involved in Ribosome Biogenesis”. In:Molecular & Cellular Proteomics7.3 (Mar. 2008), pp. 546–559.issn: 1535-9476.doi:10.1074/mcp. m700510-mcp200.url:http://dx.do...

work page doi:10.1074/mcp 2008
[6]

Erythro-megakaryocytic transcription factors associated with hereditary anemia

John D. Crispino and Mitchell J. Weiss. “Erythro-megakaryocytic transcription factors associated with hereditary anemia”. In:Blood123.20 (May 2014), pp. 3080–3088.issn: 1528-0020.doi:10.1182/blood-2014-01-453167.url: http://dx.doi.org/10.1182/blood-2014-01-453167

work page doi:10.1182/blood-2014-01-453167.url: 2014
[7]

scGPT: toward building a foundation model for single-cell multi-omics using generative AI

Haotian Cui, Chloe Wang, Hassaan Maan, Kuan Pang, Fengning Luo, Nan Duan, and Bo Wang. “scGPT: toward building a foundation model for single-cell multi-omics using generative AI”. In:Nature Methods21.8 (Feb. 2024), pp. 1470–1480.issn: 1548-7105.doi:10.1038/s41592-024-02201-0

work page doi:10.1038/s41592-024-02201-0 2024
[8]

Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development

Jose Davila-Velderrain, Juan C. Martinez-Garcia, and Elena R. Alvarez-Buylla. “Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development”. In:Frontiers in Genetics6 (Apr. 2015).issn: 1664-8021.doi:10.3389/fgene.2015.00160.url:http://dx.doi.org/10.3389/fgene.2015.00160

work page doi:10.3389/fgene.2015.00160.url:http://dx.doi.org/10.3389/fgene.2015.00160 2015
[9]

Pattern Recog- nition153, 110500 (2024).https://doi.org/https://doi.org/10.1016/j

Atray Dixit, Oren Parnas, Biyu Li, Jenny Chen, Charles P. Fulco, Livnat Jerby-Arnon, Nemanja D. Marjanovic, Danielle Dionne, Tyler Burks, Raktima Raychowdhury, Britt Adamson, Thomas M. Norman, Eric S. Lander, Jonathan S. Weissman, Nir Friedman, and Aviv Regev. “Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled ge...

work page doi:10.1016/j 2016
[10]

The new frontier of genome engineering with CRISPR-Cas9

Jennifer A. Doudna and Emmanuelle Charpentier. “The new frontier of genome engineering with CRISPR-Cas9”. In:Science346.6213 (Nov. 2014).issn: 1095-9203.doi:10.1126/science.1258096.url:http://dx.doi.org/10. 1126/science.1258096

work page doi:10.1126/science.1258096.url:http://dx.doi.org/10 2014
[11]

Stem Cell States, Fates, and the Rules of At- traction

Tariq Enver, Martin Pera, Carsten Peterson, and Peter W. Andrews. “Stem Cell States, Fates, and the Rules of At- traction”. In:Cell Stem Cell4.5 (May 2009), pp. 387–397.issn: 1934-5909.doi:10.1016/j.stem.2009.04.011.url: http://dx.doi.org/10.1016/j.stem.2009.04.011

work page doi:10.1016/j.stem.2009.04.011.url: 2009
[12]

Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks

Atefeh Taherian Fard, Sriganesh Srihari, Jessica C Mar, and Mark A Ragan. “Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks”. In:npj Systems Biology and Applications2.1 (Feb. 2016).issn: 2056-7189.doi:10.1038/npjsba.2016.1.url:http://dx.doi.org/10.1038/npjsba.2016.1

work page doi:10.1038/npjsba.2016.1.url:http://dx.doi.org/10.1038/npjsba.2016.1 2016
[13]

Bistability, Bifurcations, and Waddington’s Epigenetic Landscape

James E. Ferrell. “Bistability, Bifurcations, and Waddington’s Epigenetic Landscape”. In:Current Biology22.11 (June 2012), R458–R466.issn: 0960-9822.doi:10.1016/j.cub.2012.03.045.url:http://dx.doi.org/10.1016/j. cub.2012.03.045

work page doi:10.1016/j.cub.2012.03.045.url:http://dx.doi.org/10.1016/j 2012
[14]

Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia

Joseph A. Fraietta, Simon F. Lacey, Elena J. Orlando, Iulian Pruteanu-Malinici, Mercy Gohil, Stefan Lundh, Alina C. Boesteanu, Yan Wang, Roddy S. O’Connor, Wei-Ting Hwang, Edward Pequignot, David E. Ambrose, Changfeng Zhang, Nicholas Wilcox, Felipe Bedoya, Corin Dorfmeier, Fang Chen, Lifeng Tian, Harit Parakandi, Minnal Gupta, Regina M. Young, F. Brad Joh...

work page doi:10.1038/s41591-018-0010-1.url:http://dx.doi.org/10.1038/s41591- 2018
[15]

C/ebp𝛼induces pu.1 and interacts with ap-1 and nf-𝜅b to regulate myeloid development

Alan D. Friedman. “C/ebp𝛼induces pu.1 and interacts with ap-1 and nf-𝜅b to regulate myeloid development”. In: Blood Cells, Molecules, and Diseases39.3 (2007), pp. 340–343.issn: 1079-9796.doi:10.1016/j.bcmd.2007.06.010

work page doi:10.1016/j.bcmd.2007.06.010 2007
[16]

International Society for Cellular Therapy perspective on immune functional assays for mesenchymal stromal cells as potency release criterion for 13 advanced phase clinical trials

Jacques Galipeau, Mauro Krampera, John Barrett, Francesco Dazzi, Robert J. Deans, Joost DeBruijn, Massimo Do- minici, Willem E. Fibbe, Adrian P. Gee, Jeffery M. Gimble, Peiman Hematti, Mickey B.C. Koh, Katarina LeBlanc, Ivan Martin, Ian K. McNiece, Michael Mendicino, Steve Oh, Luis Ortiz, Donald G. Phinney, Valerie Planat, Yufang Shi, David F. Stroncek, S...

work page doi:10.1016/j.jcyt.2015.11.008 2016
[17]

Release of the export adapter, Nmd3p, from the 60S ribosomal subunit requires Rpl10p and the cytoplasmic GTPase Lsg1p

John Hedges, Matthew West, and Arlen W Johnson. “Release of the export adapter, Nmd3p, from the 60S ribosomal subunit requires Rpl10p and the cytoplasmic GTPase Lsg1p”. In:The EMBO Journal24.3 (Jan. 2005), pp. 567–579. issn: 1460-2075.doi:10.1038/sj.emboj.7600547.url:http://dx.doi.org/10.1038/sj.emboj.7600547

work page doi:10.1038/sj.emboj.7600547.url:http://dx.doi.org/10.1038/sj.emboj.7600547 2005
[18]

The unfolded protein response: controlling cell fate decisions under ER stress and beyond

Claudio Hetz. “The unfolded protein response: controlling cell fate decisions under ER stress and beyond”. In: Nature Reviews Molecular Cell Biology13.2 (Jan. 2012), pp. 89–102.issn: 1471-0080.doi:10.1038/nrm3270.url: http://dx.doi.org/10.1038/nrm3270

work page doi:10.1038/nrm3270.url: 2012
[19]

Pertpy: an end-to-end framework for perturbation analysis

Lukas Heumos, Yuge Ji, Lilly May, Tessa D. Green, Stefan Peidli, Xinyue Zhang, Xichen Wu, Johannes Ostner, Antonia Schumacher, Karin Hrovatin, Michaela Müller, Faye Chong, Gregor Sturm, Alejandro Tejada, Emma Dann, Mingze Dong, Gonçalo Pinto, Mojtaba Bahrami, Ilan Gold, Sergei Rybakov, Altana Namsaraeva, Amir Ali Moinfar, Zihe Zheng, Eljas Roellin, Isra M...

work page doi:10.1038/s41592-025-02909-7.url:http://dx.doi.org/10.1038/s41592-025-02909-7 2025
[20]

A Spliceosomal Intron Binding Protein, IBP160, Links Position-Dependent Assembly of Intron-Encoded Box C/D snoRNP to Pre- mRNA Splicing

Tetsuro Hirose, Takashi Ideue, Misato Nagai, Masatoshi Hagiwara, Mei-Di Shu, and Joan A. Steitz. “A Spliceosomal Intron Binding Protein, IBP160, Links Position-Dependent Assembly of Intron-Encoded Box C/D snoRNP to Pre- mRNA Splicing”. In:Molecular Cell23.5 (Sept. 2006), pp. 673–684.issn: 1097-2765.doi:10.1016/j.molcel.2006. 07.011.url:http://dx.doi.org/1...

work page doi:10.1016/j.molcel.2006 2006
[21]

CCAN Makes Multiple Contacts with Centromeric DNA to Provide Distinct Pathways to the Outer Kinetochore

Tetsuya Hori, Miho Amano, Aussie Suzuki, Chelsea B. Backer, Julie P. Welburn, Yimin Dong, Bruce F. McEwen, Wei-Hao Shang, Emiko Suzuki, Katsuya Okawa, Iain M. Cheeseman, and Tatsuo Fukagawa. “CCAN Makes Multiple Contacts with Centromeric DNA to Provide Distinct Pathways to the Outer Kinetochore”. In:Cell135.6 (Dec. 2008), pp. 1039–1052.issn: 0092-8674.doi...

work page doi:10.1016/j.cell.2008.10.019.url:http://dx.doi.org/10.1016/j 2008
[22]

Reprogramming cell fates: reconciling rarity with robustness

Sui Huang. “Reprogramming cell fates: reconciling rarity with robustness”. In:BioEssays31.5 (Apr. 2009), pp. 546– 560.issn: 1521-1878.doi:10.1002/bies.200800189.url:http://dx.doi.org/10.1002/bies.200800189

work page doi:10.1002/bies.200800189.url:http://dx.doi.org/10.1002/bies.200800189 2009
[23]

CRISPR-Cas9 Structures and Mechanisms

Fuguo Jiang and Jennifer A. Doudna. “CRISPR-Cas9 Structures and Mechanisms”. In:Annual Review of Biophysics 46.1 (May 2017), pp. 505–529.issn: 1936-1238.doi:10 . 1146 / annurev - biophys - 062215 - 010822.url:http : //dx.doi.org/10.1146/annurev-biophys-062215-010822

work page doi:10.1146/annurev-biophys-062215-010822 2017
[24]

A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity

Martin Jinek, Krzysztof Chylinski, Ines Fonfara, Michael Hauer, Jennifer A. Doudna, and Emmanuelle Charpentier. “A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity”. In:Science337.6096 (Aug. 2012), pp. 816–821.issn: 1095-9203.doi:10.1126/science.1225829.url:http://dx.doi.org/10.1126/ science.1225829

work page doi:10.1126/science.1225829.url:http://dx.doi.org/10.1126/ 2012
[25]

Dissecting cell identity via network inference and in silico gene perturbation

Kenji Kamimoto, Blerta Stringa, Christy M. Hoffmann, Kunal Jindal, Lilianna Solnica-Krezel, and Samantha A. Mor- ris. “Dissecting cell identity via network inference and in silico gene perturbation”. In:Nature614.7949 (Feb. 2023), pp. 742–751.issn: 1476-4687.doi:10.1038/s41586-022-05688-9.url:http://dx.doi.org/10.1038/s41586- 022-05688-9

work page doi:10.1038/s41586-022-05688-9.url:http://dx.doi.org/10.1038/s41586- 2023
[26]

Biological robustness

Hiroaki Kitano. “Biological robustness”. In:Nature Reviews Genetics5.11 (2004), pp. 826–837.issn: 1471-0064.doi: 10.1038/nrg1471

work page doi:10.1038/nrg1471 2004
[27]

Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements

Michael Kosicki, Kärt Tomberg, and Allan Bradley. “Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements”. In:Nature Biotechnology36.8 (July 2018), pp. 765–771.issn: 1546- 1696.doi:10.1038/nbt.4192.url:http://dx.doi.org/10.1038/nbt.4192

work page doi:10.1038/nbt.4192.url:http://dx.doi.org/10.1038/nbt.4192 2018
[28]

The ER chaperone and signaling regulator GRP78/BiP as a monitor of endoplasmic reticulum stress

Amy S. Lee. “The ER chaperone and signaling regulator GRP78/BiP as a monitor of endoplasmic reticulum stress”. In:Methods35.4 (Apr. 2005), pp. 373–381.issn: 1046-2023.doi:10 . 1016 / j . ymeth . 2004 . 10 . 010.url:http : //dx.doi.org/10.1016/j.ymeth.2004.10.010

work page doi:10.1016/j.ymeth.2004.10.010 2005
[29]

Chromothripsis as an on-target consequence of CRISPR–Cas9 genome editing

Mitchell L. Leibowitz, Stamatis Papathanasiou, Phillip A. Doerfler, Logan J. Blaine, Lili Sun, Yu Yao, Cheng-Zhong Zhang, Mitchell J. Weiss, and David Pellman. “Chromothripsis as an on-target consequence of CRISPR–Cas9 genome editing”. In:Nature Genetics53.6 (Apr. 2021), pp. 895–905.issn: 1546-1718.doi:10.1038/s41588-021-00838-7. url:http://dx.doi.org/10....

work page doi:10.1038/s41588-021-00838-7 2021
[30]

Dy- namics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape

Qin Li, Anders Wennborg, Erik Aurell, Erez Dekel, Jie-Zhi Zou, Yuting Xu, Sui Huang, and Ingemar Ernberg. “Dy- namics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape”. In:Proceedings of the National Academy of Sciences113.10 (Feb. 2016), pp. 2672–2677.issn: 1091-6490.doi:10.1073/pnas.1519210113. url:http://dx.do...

work page doi:10.1073/pnas.1519210113 2016
[31]

Quality cell therapy manufacturing by design

Yonatan Y Lipsitz, Nicholas E Timmins, and Peter W Zandstra. “Quality cell therapy manufacturing by design”. In:Nature Biotechnology34.4 (Apr. 2016), pp. 393–400.issn: 1546-1696.doi:10 . 1038 / nbt . 3525.url:http : //dx.doi.org/10.1038/nbt.3525

work page doi:10.1038/nbt.3525 2016
[32]

Predicting cellular responses to complex perturbations in high-throughput screens

Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, Jay Shendure, Jose L McFaline-Figueroa, Pierre Boyeau, F Alexander Wolf, Nafissa Yakubova, Stephan Günnemann, Cole Trapnell, David Lopez-Paz, and Fabian J Theis. “Predicting cellular resp...

work page doi:10.15252/msb.202211517.url:http://dx.doi.org/10.15252/msb 2023
[33]

R., & Origlia, L

John McCullough, Leremy A. Colf, and Wesley I. Sundquist. “Membrane Fission Reactions of the Mammalian ESCRT Pathway”. In:Annual Review of Biochemistry82.1 (June 2013), pp. 663–692.issn: 1545-4509.doi:10.1146/annurev- biochem-072909-101058.url:http://dx.doi.org/10.1146/annurev-biochem-072909-101058

work page doi:10.1146/annurev- 2013
[34]

and MARTIN, O

Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. “UMAP: Uniform Manifold Approximation and Projection”. In:Journal of Open Source Software3.29 (Sept. 2018), p. 861.issn: 2475-9066.doi:10.21105/joss. 00861.url:http://dx.doi.org/10.21105/joss.00861

work page doi:10.21105/joss 2018
[35]

A novel, erythroid cell-specific murine transcription factor that binds to the CACCC element and is related to the Krüppel family of nuclear proteins

I J Miller and J J Bieker. “A novel, erythroid cell-specific murine transcription factor that binds to the CACCC element and is related to the Krüppel family of nuclear proteins.” In:Molecular and Cellular Biology13.5 (1993), pp. 2776–2786.issn: 1098-5549.doi:10.1128/mcb.13.5.2776

work page doi:10.1128/mcb.13.5.2776 1993
[36]

Visualizing structure and transitions in high-dimensional biological data

Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B. Burkhardt, William S. Chen, Kristina Yim, An- tonia van den Elzen, Matthew J. Hirn, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf, and Smita Krishnaswamy. “Visualizing structure and transitions in high-dimensional biological data”. In:Nature Biotechnology37.12 (Dec. 2019), pp. 1482–1492...

work page doi:10.1038/s41587-019-0336-3.url:http://dx.doi.org/10.1038/ 2019
[37]

Dissecting Engineered Cell Types and Enhancing Cell Fate Conversion via CellNet

Samantha A. Morris, Patrick Cahan, Hu Li, Anna M. Zhao, Adrianna K. San Roman, Ramesh A. Shivdasani, James J. Collins, and George Q. Daley. “Dissecting Engineered Cell Types and Enhancing Cell Fate Conversion via CellNet”. In:Cell158.4 (Aug. 2014), pp. 889–902.issn: 0092-8674.doi:10.1016/j.cell.2014.07.021.url:http://dx. doi.org/10.1016/j.cell.2014.07.021

work page doi:10.1016/j.cell.2014.07.021.url:http://dx 2014
[38]

Transcriptome-wide analysis of differential expression in perturbation atlases

Ajay Nadig, Joseph M. Replogle, Angela N. Pogson, Mukundh Murthy, Steven A. McCarroll, Jonathan S. Weissman, Elise B. Robinson, and Luke J. O’Connor. “Transcriptome-wide analysis of differential expression in perturbation atlases”. In:Nature Genetics57.5 (Apr. 2025), pp. 1228–1237.issn: 1546-1718.doi:10.1038/s41588-025-02169-3. url:http://dx.doi.org/10.10...

work page doi:10.1038/s41588-025-02169-3 2025
[39]

Recapitulating endocrine cell clustering in culture promotes maturation of human stem-cell-derived𝛽cells

Gopika G. Nair, Jennifer S. Liu, Holger A. Russ, Stella Tran, Michael S. Saxton, Richard Chen, Charity Juang, Mei- lan Li, Vinh Q. Nguyen, Simone Giacometti, Sapna Puri, Yuan Xing, Yong Wang, Gregory L. Szot, Jose Oberholzer, Anil Bhushan, and Matthias Hebrok. “Recapitulating endocrine cell clustering in culture promotes maturation of human stem-cell-deri...

work page doi:10.1038/s41556-018-0271-4 2019
[40]

Exploring genetic interaction manifolds constructed from rich single-cell phenotypes

Thomas M Norman, Max A Horlbeck, Joseph M Replogle, Y Alexander Ge, Alex Xu, Marco Jost, Luke A Gilbert, and Jonathan S Weissman. “Exploring genetic interaction manifolds constructed from rich single-cell phenotypes”. In:Science365.6455 (2019), pp. 786–793.issn: 1095-9203.doi:10.1126/science.aax4438

work page doi:10.1126/science.aax4438 2019
[41]

Roles of CHOP/GADD153 in endoplasmic reticulum stress

S Oyadomari and M Mori. “Roles of CHOP/GADD153 in endoplasmic reticulum stress”. In:Cell Death & Differenti- ation11.4 (Dec. 2003), pp. 381–389.issn: 1476-5403.doi:10.1038/sj.cdd.4401373.url:http://dx.doi.org/ 10.1038/sj.cdd.4401373

work page doi:10.1038/sj.cdd.4401373.url:http://dx.doi.org/ 2003
[42]

Characterizing the molec- ular regulation of inhibitory immune checkpoints with multimodal single-cell screens

Efthymia Papalexi, Eleni P. Mimitou, Andrew W. Butler, Samantha Foster, Bernadette Bracken, William M. Mauck, Hans-Hermann Wessels, Yuhan Hao, Bertrand Z. Yeung, Peter Smibert, and Rahul Satija. “Characterizing the molec- ular regulation of inhibitory immune checkpoints with multimodal single-cell screens”. In:Nature Genetics53.3 (2021), pp. 322–331.issn:...

work page doi:10.1038/s41588-021-00778-2 2021
[43]

scPerturb: harmonized single-cell perturbation data

Stefan Peidli, Tessa D. Green, Ciyue Shen, Torsten Gross, Joseph Min, Samuele Garda, Bo Yuan, Linus J. Schumacher, Jake P. Taylor-King, Debora S. Marks, Augustin Luna, Nils Blüthgen, and Chris Sander. “scPerturb: harmonized single-cell perturbation data”. In:Nature Methods21.3 (Jan. 2024), pp. 531–540.issn: 1548-7105.doi:10 . 1038 / s41592-023-02144-y.url...

work page doi:10.1038/s41592-023-02144-y 2024
[44]

Chromatin states define tumour-specific T cell dysfunction and reprogramming

Mary Philip, Lauren Fairchild, Liping Sun, Ellen L. Horste, Steven Camara, Mojdeh Shakiba, Andrew C. Scott, Agnes Viale, Peter Lauer, Taha Merghoub, Matthew D. Hellmann, Jedd D. Wolchok, Christina S. Leslie, and Andrea Schietinger. “Chromatin states define tumour-specific T cell dysfunction and reprogramming”. In:Nature545.7655 15 (May 2017), pp. 452–456....

work page 2017
[45]

Failure of Terminal Erythroid Differentiation in EKLF-Deficient Mice Is Associated with Cell Cycle Perturbation and Reduced Expression of E2F2

Andre M. Pilon, Murat O. Arcasoy, Holly K. Dressman, Serena E. Vayda, Yelena D. Maksimova, Jose I. Sangerman, Patrick G. Gallagher, and David M. Bodine. “Failure of Terminal Erythroid Differentiation in EKLF-Deficient Mice Is Associated with Cell Cycle Perturbation and Reduced Expression of E2F2”. In:Molecular and Cellular Biology28.24 (2008), pp. 7394–74...

work page doi:10.1128/mcb.01087-08 2008
[46]

Geometric Stability: The Missing Axis of Representations

Prashant C. Raju. “Geometric Stability: The Missing Axis of Representations”. In:arXiv preprint arXiv:2601.09173 (2026)

work page internal anchor Pith review Pith/arXiv arXiv 2026
[47]

Raju.Shesha: Self-Consistency Metrics for Representational Stability

Prashant C. Raju.Shesha: Self-Consistency Metrics for Representational Stability. 2026.doi:10 . 5281 / zenodo . 18227453.url:https://doi.org/10.5281/zenodo.18227453

work page doi:10.5281/zenodo.18227453 2026
[48]

Geometry of gene regulatory dynamics

David A. Rand, Archishman Raju, Meritxell Sáez, Francis Corson, and Eric D. Siggia. “Geometry of gene regulatory dynamics”. In:Proceedings of the National Academy of Sciences118.38 (Sept. 2021).issn: 1091-6490.doi:10.1073/ pnas.2109729118.url:http://dx.doi.org/10.1073/pnas.2109729118

work page doi:10.1073/pnas.2109729118 2021
[49]

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

Joseph M. Replogle, Reuben A. Saunders, Angela N. Pogson, Jeffrey A. Hussmann, Alexander Lenail, Alina Guna, Lauren Mascibroda, Eric J. Wagner, Karen Adelman, Gila Lithwick-Yanai, Nika Iremadze, Florian Oberstrass, Doron Lipson, Jessica L. Bonnar, Marco Jost, Thomas M. Norman, and Jonathan S. Weissman. “Mapping information-rich genotype-phenotype landscap...

work page doi:10.1016/j.cell.2022.05.013 2022
[50]

Signal integration in the endoplasmic reticulum unfolded protein response

David Ron and Peter Walter. “Signal integration in the endoplasmic reticulum unfolded protein response”. In:Nature Reviews Molecular Cell Biology8.7 (July 2007), pp. 519–529.issn: 1471-0080.doi:10.1038/nrm2199.url:http: //dx.doi.org/10.1038/nrm2199

work page doi:10.1038/nrm2199.url:http: 2007
[51]

Predicting transcriptional outcomes of novel multigene perturba- tions with GEARS

Yusuf Roohani, Kexin Huang, and Jure Leskovec. “Predicting transcriptional outcomes of novel multigene perturba- tions with GEARS”. In:Nature Biotechnology42.6 (Aug. 2023), pp. 927–935.issn: 1546-1696.doi:10.1038/s41587- 023-01905-6.url:http://dx.doi.org/10.1038/s41587-023-01905-6

work page doi:10.1038/s41587- 2023
[52]

Universal Cell Embeddings: A Foundation Model for Cell Biology

Yanay Rosen, Yusuf Roohani, Ayush Agrawal, Leon Samotorcan, Tabula Sapiens Consortium, Stephen R. Quake, and Jure Leskovec. “Universal Cell Embeddings: A Foundation Model for Cell Biology”. In:bioRxiv(2023).doi: 10.1101/2023.11.28.568918

work page doi:10.1101/2023.11.28.568918 2023
[53]

Potency testing of cell and gene therapy products

Paula Salmikangas, Björn Carlsson, Christophe Klumb, Tatiana Reimer, and Steffen Thirstrup. “Potency testing of cell and gene therapy products”. In:Frontiers in Medicine10 (May 2023).issn: 2296-858X.doi:10 . 3389 / fmed . 2023.1190016.url:http://dx.doi.org/10.3389/fmed.2023.1190016

work page doi:10.3389/fmed.2023.1190016 2023
[54]

The multifunctional role of EKLF/KLF1 during erythropoiesis

Miroslawa Siatecka and James J. Bieker. “The multifunctional role of EKLF/KLF1 during erythropoiesis”. In:Blood 118.8 (2011), pp. 2044–2054.issn: 1528-0020.doi:10.1182/blood-2011-03-331371

work page doi:10.1182/blood-2011-03-331371 2011
[55]

Waddington’s canalization revisited: Developmental stability and evolution

Mark L. Siegal and Aviv Bergman. “Waddington’s canalization revisited: Developmental stability and evolution”. In:Proceedings of the National Academy of Sciences99.16 (June 2002), pp. 10528–10532.issn: 1091-6490.doi:10. 1073/pnas.102303999.url:http://dx.doi.org/10.1073/pnas.102303999

work page doi:10.1073/pnas.102303999 2002
[56]

Mechanism of action, potency and efficacy: considerations for cell therapies

Carl G. Simon, Erich H. Bozenhardt, Christina M. Celluzzi, David Dobnik, Melanie L. Grant, Uma Lakshmipathy, Thiana Nebel, Linda Peltier, Anthony Ratcliffe, James L. Sherley, Glyn N. Stacey, Rouzbeh R. Taghizadeh, Eddie H. P. Tan, and Sandrine Vessillier. “Mechanism of action, potency and efficacy: considerations for cell therapies”. In:Journal of Transla...

work page doi:10.1186/s12967-024-05179-7.url: 2024
[57]

Conrad Hal Waddington: the last Renaissance biologist?

Jonathan M. W. Slack. “Conrad Hal Waddington: the last Renaissance biologist?” In:Nature Reviews Genetics3.11 (Nov. 2002), pp. 889–895.issn: 1471-0064.doi:10.1038/nrg933.url:http://dx.doi.org/10.1038/nrg933

work page doi:10.1038/nrg933.url:http://dx.doi.org/10.1038/nrg933 2002
[58]

Decoding heterogeneous single-cell perturbation responses

Bicna Song, Dingyu Liu, Weiwei Dai, Natalie F. McMyn, Qingyang Wang, Dapeng Yang, Adam Krejci, Anatoly Vasilyev, Nicole Untermoser, Anke Loregger, Dongyuan Song, Breanna Williams, Bess Rosen, Xiaolong Cheng, Lumen Chao, Hanuman T. Kale, Hao Zhang, Yarui Diao, Tilmann Bürckstümmer, Janet D. Siliciano, Jingyi Jessica Li, Robert F. Siliciano, Danwei Huangfu,...

work page doi:10.1038/s41556- 2025
[59]

Pancreatic𝛽Cell Dedifferentiation as a Mechanism of Diabetic𝛽Cell Failure

Chutima Talchai, Shouhong Xuan, Hua V. Lin, Lori Sussel, and Domenico Accili. “Pancreatic𝛽Cell Dedifferentiation as a Mechanism of Diabetic𝛽Cell Failure”. In:Cell150.6 (Sept. 2012), pp. 1223–1234.issn: 0092-8674.doi:10.1016/ j.cell.2012.07.029.url:http://dx.doi.org/10.1016/j.cell.2012.07.029

work page doi:10.1016/j.cell.2012.07.029 2012
[60]

KLF1 directly coordinates almost all aspects of terminal erythroid differentiation

Michael R. Tallack and Andrew C. Perkins. “KLF1 directly coordinates almost all aspects of terminal erythroid differentiation”. In:IUBMB Life62.12 (2010), pp. 886–890.issn: 1521-6551.doi:10.1002/iub.404. 16

work page doi:10.1002/iub.404 2010
[61]

A global role for KLF1 in erythropoiesis revealed by ChIP-seq in primary erythroid cells

Michael R. Tallack, Tom Whitington, Wai Shan Yuen, Elanor N. Wainwright, Janelle R. Keys, Brooke B. Gardiner, Ehsan Nourbakhsh, Nicole Cloonan, Sean M. Grimmond, Timothy L. Bailey, and Andrew C. Perkins. “A global role for KLF1 in erythropoiesis revealed by ChIP-seq in primary erythroid cells”. In:Genome Research20.8 (2010), pp. 1052–1063.issn: 1088-9051....

work page doi:10.1101/gr.106575.110 2010
[62]

The Human Homologue of Bub3 Is Required for Kinetochore Localization of Bub1 and a Mad3/Bub1-related Protein Kinase

Stephen S. Taylor, Edward Ha, and Frank McKeon. “The Human Homologue of Bub3 Is Required for Kinetochore Localization of Bub1 and a Mad3/Bub1-related Protein Kinase”. In:The Journal of Cell Biology142.1 (July 1998), pp. 1–11.issn: 1540-8140.doi:10.1083/jcb.142.1.1.url:http://dx.doi.org/10.1083/jcb.142.1.1

work page doi:10.1083/jcb.142.1.1.url:http://dx.doi.org/10.1083/jcb.142.1.1 1998
[63]

Transfer learning enables predictions in network biology

Christina V. Theodoris, Ling Xiao, Anant Chopra, Mark D. Chaffin, Zeina R. Al Sayed, Matthew C. Hill, Helene Mantineo, Elizabeth M. Brydon, Zexian Zeng, X. Shirley Liu, and Patrick T. Ellinor. “Transfer learning enables predictions in network biology”. In:Nature618.7965 (May 2023), pp. 616–624.issn: 1476-4687.doi:10 . 1038 / s41586-023-06139-9

work page 2023
[64]

GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas9 nucleases

Shengdar Q Tsai, Zongli Zheng, Nhu T Nguyen, Matthew Liebers, Ved V Topkar, Vishal Thapar, Nicolas Wyvekens, Cyd Khayter, A John Iafrate, Long P Le, Martin J Aryee, and J Keith Joung. “GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas9 nucleases”. In:Nature Biotechnology33.2 (Dec. 2014), pp. 187–197. issn: 1546-1696.doi:10.1038/...

work page doi:10.1038/nbt.3117.url:http://dx.doi.org/10.1038/nbt.3117 2014
[65]

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Koki Tsuyuzaki, Hiroyuki Sato, Kenta Sato, and Itoshi Nikaido. “Benchmarking principal component analysis for large-scale single-cell RNA-sequencing”. In:Genome Biology21.1 (Jan. 2020).issn: 1474-760X.doi:10 . 1186 / s13059-019-1900-3.url:http://dx.doi.org/10.1186/s13059-019-1900-3

work page doi:10.1186/s13059-019-1900-3 2020
[66]

The scverse project provides a computational ecosystem for single-cell omics data analysis

Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Philipp Angerer, Volker Bergen, Pierre Boyeau, Maren Büttner, Gokcen Eraslan, David Fischer, Max Frank, Justin Hong, Michal Klein, Marius Lange, Romain Lopez, Mohammad Lotfollahi, Malte D. Luecken, Fidel Ramirez, Jeffrey Regier, Sergei R...

work page doi:10.1038/s41587-023- 2023
[67]

Journal of Open Source Software9(101), 4371 (2024) https://doi.org/10.21105/joss.04371

Isaac Virshup, Sergei Rybakov, Fabian J. Theis, Philipp Angerer, and F. Alexander Wolf. “anndata: Access and store annotated data matrices”. In:Journal of Open Source Software9.101 (Sept. 2024), p. 4371.issn: 2475-9066.doi:10. 21105/joss.04371.url:http://dx.doi.org/10.21105/joss.04371

work page doi:10.21105/joss.04371 2024
[68]

A guide to manufacturing CAR T cell therapies

Philipp Vormittag, Rebecca Gunn, Sara Ghorashian, and Farlan S Veraitch. “A guide to manufacturing CAR T cell therapies”. In:Current Opinion in Biotechnology53 (Oct. 2018), pp. 164–181.issn: 0958-1669.doi:10 . 1016 / j . copbio.2018.01.025.url:http://dx.doi.org/10.1016/j.copbio.2018.01.025

work page doi:10.1016/j.copbio.2018.01.025 2018
[69]

London: George Allen & Unwin, 1957

Conrad Hal Waddington.The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology. London: George Allen & Unwin, 1957

work page 1957
[70]

The Unfolded Protein Response: From Stress Pathway to Homeostatic Regulation

Peter Walter and David Ron. “The Unfolded Protein Response: From Stress Pathway to Homeostatic Regulation”. In:Science334.6059 (Nov. 2011), pp. 1081–1086.issn: 1095-9203.doi:10 . 1126 / science . 1209038.url:http : //dx.doi.org/10.1126/science.1209038

work page doi:10.1126/science.1209038 2011
[71]

Quantifying the Waddington landscape and biological paths for development and differentiation

Jin Wang, Kun Zhang, Li Xu, and Erkang Wang. “Quantifying the Waddington landscape and biological paths for development and differentiation”. In:Proceedings of the National Academy of Sciences108.20 (May 2011), pp. 8257– 8262.issn: 1091-6490.doi:10.1073/pnas.1017017108.url:http://dx.doi.org/10.1073/pnas.1017017108

work page doi:10.1073/pnas.1017017108.url:http://dx.doi.org/10.1073/pnas.1017017108 2011
[72]

Transient rest restores functionality in exhausted CAR-T cells through epigenetic remodeling

Evan W. Weber, Kevin R. Parker, Elena Sotillo, Rachel C. Lynn, Hima Anbunathan, John Lattin, Zinaida Good, Julia A. Belk, Bence Daniel, Dorota Klysz, Meena Malipatlolla, Peng Xu, Malek Bashti, Sabine Heitzeneder, Louai Labanieh, Panayiotis Vandris, Robbie G. Majzner, Yanyan Qi, Katalin Sandor, Ling-Chun Chen, Snehit Prabhu, Andrew J. Gentles, Thomas J. Wa...

work page doi:10.1126/science.aba1786.url:http://dx.doi.org/10.1126/science.aba1786 2021
[73]

Lineage tracing on tran- scriptional landscapes links state to fate during differentiation

Caleb Weinreb, Alejo Rodriguez-Fraticelli, Fernando D. Camargo, and Allon M. Klein. “Lineage tracing on tran- scriptional landscapes links state to fate during differentiation”. In:Science367.6479 (Feb. 2020).issn: 1095-9203. doi:10.1126/science.aaw3381.url:http://dx.doi.org/10.1126/science.aaw3381

work page doi:10.1126/science.aaw3381.url:http://dx.doi.org/10.1126/science.aaw3381 2020
[74]

Alexander Wolf, Philipp Angerer, and Fabian J

F. Alexander Wolf, Philipp Angerer, and Fabian J. Theis. “SCANPY: large-scale single-cell gene expression data analysis”. In:Genome Biology19.1 (2018).issn: 1474-760X.doi:10.1186/s13059-017-1382-0

work page doi:10.1186/s13059-017-1382-0 2018
[75]

BLVRB redox mutation defines heme degradation in a metabolic 17 pathway of enhanced thrombopoiesis in humans

Song Wu, Zongdong Li, Dmitri V. Gnatenko, Beibei Zhang, Lu Zhao, Lisa E. Malone, Nedialka Markova, Timothy J. Mantle, Natasha M. Nesbitt, and Wadie F. Bahou. “BLVRB redox mutation defines heme degradation in a metabolic 17 pathway of enhanced thrombopoiesis in humans”. In:Blood128.5 (Aug. 2016), pp. 699–709.issn: 1528-0020.doi: 10.1182/blood-2016-02-69699...

work page doi:10.1182/blood-2016-02-696997.url:http://dx.doi.org/10.1182/blood-2016-02-696997 2016
[76]

Predicting Pancreas Cell Fate Decisions and Reprogramming with a Hierarchical Multi-Attractor Model

Joseph Xu Zhou, Lutz Brusch, and Sui Huang. “Predicting Pancreas Cell Fate Decisions and Reprogramming with a Hierarchical Multi-Attractor Model”. In:PLoS ONE6.3 (Mar. 2011). Ed. by Kumar Selvarajoo, e14752.issn: 1932-6203. doi:10.1371/journal.pone.0014752.url:http://dx.doi.org/10.1371/journal.pone.0014752

work page doi:10.1371/journal.pone.0014752.url:http://dx.doi.org/10.1371/journal.pone.0014752 2011
[77]

Hyperbolic geometry of gene expression

Yuansheng Zhou and Tatyana O. Sharpee. “Hyperbolic geometry of gene expression”. In:iScience24.3 (Mar. 2021), p. 102225.issn: 2589-0042.doi:10.1016/j.isci.2021.102225.url:http://dx.doi.org/10.1016/j.isci. 2021.102225. 18 Extended Methods Datasets and preprocessing Five single-cell CRISPR perturbation datasets were accessed via the pertpy Python package (v...

work page doi:10.1016/j.isci.2021.102225.url:http://dx.doi.org/10.1016/j.isci 2021
[78]

Quality filtering: cells with fewer than 100 detected genes were removed

work page
[79]

Library-size normalization (Wolf, Angerer, and Theis 2018):scanpy.pp.normalize_total()with default param- eters (Norman, Dixit, Papalexi) ortarget_sum=1e4(Replogle, Adamson)

work page 2018
[80]

Log transformation:scanpy.pp.log1p()

work page

Showing first 80 references.