Molecular Systems Biology

Cells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computational traditions describe this behaviour: mechanistic differential-equation models and representation-learning methods. Mechanistic models encode pathway topology and kinetics but cannot easily represent variation arising outside the modelled pathway. Representation learning, instead, maps genome-wide measurements onto low-dimensional manifolds but offers no mechanistic account of how the resulting cell states execute their functions. Reconciling these views, explaining signalling heterogeneity in a manner that is at once data-driven and mechanistically interpretable, has remained difficult. Here we introduce deep mechanistic models (DMMs), which couple semi-supervised representation learning to an ordinary-differential-equation model of EGFR/MAPK signalling, trained end-to-end so that the learnt representation and mechanistic parametrisation inform each other. Applying DMMs to multiplexed signalling data from 63 breast cancer cell lines, we show that the models generalise to held-out cell lines and attribute most heterogeneity to pathway-extrinsic factors, namely baseline ERBB2 activation and a Ca{superscript 2}/p38 signalling axis, rather than to variation in core MAPK components. Where the models fail, the discrepancies pinpoint rare signalling-altering mutations and recurrent programmes, including a putative AMPK- BRAF MEK-inhibitor-resistance axis and a cytoskeletal programme. We further find that mechanistic integration of EGFR receptor levels reshapes the learnt representation, rendering a molecular and a systems-level account of the same data equivalent in predictive power. DMMs thus offer a general framework for fusing mechanism with learning, naturally extendable to further modalities such as imaging, and simultaneously turn model failure into a systematic route to discover and evaluate candidate biology.

Metabolic profiles are used to quantify disturbance, classify disease and infer biochemical function, yet these endpoints retain different information. We combine secondary analysis of 386 metabolites in six Arabidopsis thaliana mutation accumulation lines, known-disease classification in 129 public human metabolic-disease profiles, and an analytically solvable kinetic model. A conditional occupancy model separates annotation-bin breadth from hit count and measured bin sizes: the plant low-versus-high fitness comparison changes from a two-sided exact permutation p = 0.10 to p = 0.30 after calibration. Unsigned metabolite profiles achieve mean macro recall of 0.911 and 0.895 in separate EDTA and heparin subsets, compared with 0.624 and 0.382 for a burden baseline. However, controlled loss of 50% of held-out measurements reverses representation ranking in heparin, giving signed profiles a recall advantage of 0.108 with a conditional bootstrap interval of 0.063-0.155. A spectral decomposition identifies the within-pathway contrasts attenuated by smoothing. The kinetic model then shows that lower concentration and faster relaxation can coexist with reduced desired product flux, while a null-space criterion states when observations identify a metabolic task. The results connect assay coverage, diagnostic information and functional identifiability. Human performance is internally validated, and the kinetic predictions remain uncalibrated to patients.

Bacterial growth laws quantitatively connect intracellular resource allocation to growth rate, enabling accurate predictions of physiology and antibiotic responses. Yet these laws have been rigorously tested for only a handful of perturbations. Here, we show that the growth law linking ribosome levels to growth rate under translation-inhibiting antibiotics is not universal, but rather depends on the antibiotic's mechanism of action. Quantitative proteomics across finely resolved one- and two-dimensional antibiotic gradients showed that inhibitors of translocation elongation or peptide bond formation elicit the canonical rise in ribosome levels, consistent with the growth law. By contrast, antibiotics disrupting translation initiation or fidelity produced distinct responses without ribosome upregulation. The transcription inhibitor rifampicin even reduced ribosome abundance. Combining antibiotics with divergent ribosome responses revealed a generalized growth law, in which the individual responses to perturbations superimpose. Embedding this law in a mathematical model explains distinct drug interaction patterns observed between rifampicin and different translation inhibitors. A low-dimensional structure pervades the entire proteome, enabling prediction of responses to drug pairs based on single-drug measurements. Together, these findings broaden the scope of bacterial growth laws and provide new principles for predicting responses to antibiotic combinations.

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

The coordination of molecular networks defines cellular functions. This is reflected in molecular covariation within a cell type, which is more subtle than the differences separating cell types and has therefore been difficult to quantify. To achieve the depth, consistency and accuracy required to resolve such covariation, we leveraged single-cell proteomics (plexDIA) and transcriptomics (Smart-seq3xpress) to analyze the proteomes and transcriptomes of thousands of single peripheral blood mononuclear cells (PBMCs). plexDIA quantified more protein-coding gene products per cell with higher data completeness while Smart-seq3xpress quantified more gene products across all cells. The protein measurements revealed cell-type-specific proteome architecture -- shaped in part by protein stability, and complex coordination -- that was undetectable in our mRNA data. Specifically, protein covariation within a cell type suggests cell-type-specific protein-protein interactions and functional rewiring of biological pathways independent of previously characterized abundance differences. Covariation analysis within cell types resolves a B cell-specific axis of translational states inversely covarying with GDF6 cytokine abundance. Our framework captures cell-type-specific functional coordination representing a distinct information layer accessible via single-cell proteomics.

The rapid growth of omics studies offers new opportunities to uncover disease mechanisms, yet findings from individual studies or single omics may be influenced by cohort-, platform- and layer-specific variation. Integrating evidence across studies and omics layers can reveal robust biological signals that are reproducible across molecular layers, but differences in assay platforms, sample sizes and feature coverage complicate direct combination of summary statistics. Here we present the Omics Rank-Based Integration Tool (ORBIT), a direction-aware framework for prioritizing concordant signals across omics layers using only within-dataset feature ranks and effect directions. ORBIT tests directional rank consistency across omics layers against a closed-form variance-gamma null distribution while accounting for inter-dataset correlation and incomplete feature overlap. Simulations show that ORBIT maintains well-calibrated false-positive rates under inter-omics correlation, increasing numbers of omics layers and missingness, while detecting concordant signals with increasing power as the number of layers grows. We applied ORBIT to tubulointerstitial transcriptomic data from patients with chronic kidney disease in the C-PROBE cohort and proteomic data from the Kidney Precision Medicine Project, identifying a concordant injury programme marked by inflammatory and fibrotic remodeling together with impaired energy metabolism. We also used ORBIT to integrate ten transcriptomic, four proteomic and one translatomic datasets in dilated cardiomyopathy heart tissue, identifying extracellular matrix remodeling and mitochondrial energy failure as the dominant concordant biological programmes. In both diseases, ORBIT prioritized concordant genes and identified pathways that were not significant in any single omics layer alone. These results show that directional rank integration can be used for integrating heterogeneous omics summary data and prioritizing robust molecular mechanisms.

Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states and prioritizing virtual perturbations toward defined biological endpoints. MEGA-ODE integrates molecular-network priors, graph neural ordinary differential equations and context-adaptive mixture-of-experts routing. In L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE improved held-out-feature and unseen-perturbation prediction over baseline methods, and in SARS-CoV-2 infection time-series data it remained competitive for future-time-point forecasting. In a COVID-19 patient cohort, predicted intermediate profiles improved retrospective disease-stage stratification relative to observed profiles alone, while expert programs highlighted immune and inflammatory signals associated with severity. Across the MAPK drug-response and stem-cell differentiation case studies, graph- and expert-level attributions prioritized perturbation-associated MAPK edges, developmental regulators and TF-target relationships supported by independent promoter-proximal ChIP-seq overlap. In hESC-to-definitive-endoderm differentiation, MEGA-ODE prioritized candidate transcription-factor perturbations predicted to shift 12-36 h profiles toward 96 h definitive-endoderm marker signatures, framing trajectory navigation as a concrete hypothesis-generation task. Together, these results support biologically structured continuous-time modeling for prediction, interpretation and virtual-perturbation prioritization from sparse temporal omics data.

Huntington disease (HD) is caused by a polyglutamine expanded huntingtin protein that exerts progressive cellular toxicity. However, the temporal sequence of pathogenic, particularly early and reversible versus late and irreversible events remain incompletely defined, despite their distinct therapeutic implications. To delineate this trajectory, we profiled the proteome of a huntingtin expressing cell model at early (72 h) and late (144 h) stages. Rather than a linear progression, pathogenicity unfolded in two discrete phases. At the early stage, cells exhibited a broad activation of RNA processing, splicing, and protein synthesis machinery, consistent with an adaptive response aimed at preserving gene expression fidelity under stress. By the late stage, this compensatory program had collapsed, giving rise to a dominant failure in mitochondrial energy metabolism. Notably, 85% of proteins altered at both time points reversed direction of change between stages, indicating that mutant huntingtin reprograms cellular function wholesale rather than amplifying a fixed set of perturbations. Detailed analysis of mitochondrial respiratory complexes revealed that terminal ATP generating components (cytochrome c oxidase and ATP synthase) were severely affected, whereas upstream electron transport elements were retained or upregulated. Leveraging this proteomic map, we applied an AI assisted, direction aware drug repurposing strategy. Of 1,712 differentially expressed proteins, 498 were druggable, and 89 mapped to approved agents with mechanisms concordant with the required correction. These included Complex I targeted agents (metformin, ME 344) and mitochondria directed therapeutics (SS 31, MitoQ), several of which have previously been evaluated in HD. Collectively, these findings define a biphasic course of huntingtin toxicity and highlight an early therapeutic window in which intervention is most likely to be applied, prior to irreversible deterioration of mitochondrial respiratory function.

Translation frequency in bacteria depends on how ribosomes, mRNAs, and initiation factors are allocated across growth conditions. Here, we developed a mechanistic ODE-based model of Escherichia coli translation that represents initiation, elongation, termination, and coupled auxiliary processes. Growth-dependent abundances were derived from physiological relationships and reprocessed omics data, and simulated outputs were compared with translation-frequency and active-ribosome references. The model predicts a continuous shift from complex-formation-limited toward ribosome-limited behavior as growth increases. This shift is characterized by a decline in free-ribosome abundance, whereas initiation-factor pools remain largely unbound and do not become depleted in parallel. Together with the implemented IF-dependent kinetic term, this preserved availability provides a model-internal route through which productive initiation can be maintained despite increasing ribosome utilization. Consistently, transcript-wide ribosome loading remains below its theoretical maximum, while COG-level simulations reveal distinct sector-specific translation-frequency trajectories. The study therefore provides a resource-allocation framework for interpreting how mRNA--ribosome interactions shape bacterial translation across growth conditions.

Objective Single-cell RNA sequencing has transformed our understanding of cellular heterogeneity within osteoarthritic (OA) joint tissues, yet transcript abundance provides only an indirect measure of cellular phenotype and function. Recent advances in mass spectrometry now enable proteins to be measured directly at single-cell resolution. Here, we applied single-cell proteomics (SCP) to primary human OA cartilage and synovium to determine whether cellular heterogeneity within the diseased joint can be resolved directly at the proteome level. Design Cartilage and synovium were obtained from patients undergoing total knee arthroplasty for OA. Low-input proteomic profiling was first performed to establish proteome depth from limited cell numbers and validate biologically relevant clustering. SCP was then applied to individual cells, followed by unsupervised clustering, differential protein abundance analysis and iterative biological annotation. Results Low-input proteomics identified 7,800 proteins from 1,000 cells while retaining tissue- and cell-associated biological differences. Across the SCP dataset, ~2,300 unique proteins were detected, resolving chondrocyte, synovial fibroblast and immune-cell populations that closely paralleled those previously defined by single-cell transcriptomics. Higher-resolution analysis distinguished lining and sublining fibroblasts, discrete immune populations and multiple protein-defined chondrocyte states. Conclusions SCP enables direct characterisation of cellular heterogeneity within the human OA joint at the level of the proteome. By independently recovering established cellular populations while revealing protein-defined chondrocyte states, our findings establish SCP as a powerful complement to single-cell transcriptomics and open a new route to investigating functional cellular states and disease mechanisms that cannot be inferred from RNA abundance alone.

Despite advances in proteomics, the proteome is still often viewed as a passive reflection of the transcriptome. Using Mycoplasma gallisepticum as a minimal cell model, we analyzed large-scale datasets across multiple stress conditions to investigate whether the proteome functions as an integrated regulatory system. The proteome remained quantitatively stable under stress, yet the bacteria adapted successfully. We identified a minimal core of 17 major proteins constituting 35% of cellular protein mass. These proteins exhibit distinct physicochemical properties--higher positive charge, lower hydrophilicity, and enrichment in intrinsically disordered regions--suggesting they shape cytoplasmic organization and may drive liquid-liquid phase separation. Minor proteins form tightly correlated clusters, while stress conditions trigger rearrangement of protein complexes without abundance changes, representing an energy-efficient adaptation strategy. Cross linking mass spectrometry revealed condition specific interactome remodeling, which may underlie mycoplasma adaptation to diverse stresses. Our findings suggest that the proteome operates as an active cellular organizer, where physicochemical properties and dynamic complex rearrangements enable adaptation independently of transcriptional control. This framework may inform synthetic biology efforts to engineer minimal cells.

Formalin-fixed paraffin-embedded (FFPE) tissue is the dominant clinical pathology resource yet whether it faithfully preserves organ signalling biology and supports directional regulatory analysis remains unquantified. We generated a phosphoproteome map from eight healthy rat organs, separating preservation effects from biological variation. Using mass spectrometry, we quantified 54,710 phosphosites on 5,994 proteins across receptors, kinase cascades and nuclear regulators. Organ-specific phosphosite signatures matched known physiological and proliferative states. Paired antagonistic phosphosites converted into "activating-minus-inhibitory" indices that quantified net tissue-specific pathway activity, while a "kinase-by-organ activity" matrix resolved functional hierarchies. Joint analysis with an external fresh-frozen phosphoproteome dataset yielded 58,631 phosphosites total, recovering 86% of the 28,888 sites detected in the frozen dataset. Organ identity explained over 92% of the total variance after batch correction, versus under 0.5% for preservation method. Per-organ phosphosite intensities agreed closely between preservation modes except in brain. This establishes that archived pathology tissue supports biologically faithful phosphoproteome analysis at organ, pathway, and site resolution, providing a framework for retrospective signalling studies in clinical archives. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/741173v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@7ec1forg.highwire.dtl.DTLVardef@1f22bcorg.highwire.dtl.DTLVardef@217f5forg.highwire.dtl.DTLVardef@1316b61_HPS_FORMAT_FIGEXP M_FIG C_FIG

High-content imaging-based screens with small molecule or genetic perturbations rely on rich optical phenotypes, yet how distinct subcellular markers contribute complementary biological information remains unclear. Here, we introduce CellMAPP, a multiplexed organelle imaging platform that uses optical pooled screens with iterative antibody staining to map cellular architecture under perturbation. Using 17 markers across 524 genetic perturbations in MCF-7 cells, we systematically examined how individual, and combinations of, markers capture diverse morphological responses. We find that markers provide complementary views of perturbation effects, with increased multiplexing improving signal and the breadth of hit recovery, although some phenotypes are better recovered from a subset of features rather than a full profile. Because non-molecular image-based profiles can be challenging to link with interpretable biology, we developed CHART (available at https://github.com/Genentech/CHART), an archetype analysis framework that identifies distinct reference morphological states for each marker and uses multimodal large language models (MLLMs) to automatically annotate them, connecting cellular images to recognizable organelle phenotypes. Integrating archetypes across multiple markers revealed modular morphological programs that describe coordinated organelle reorganization in response to perturbation. This framework identified regulators of defined organelle states such as micronuclei, nominated novel gene functions and revealed complex patterns of multi-organelle reposition. Together, optical profiling with MAPP and CHART provides an interpretable, scalable framework for image-based screens that sheds light on rules governing cellular structural reprogramming.

Lung adenocarcinoma (LUAD) develops through a defined morphological sequence from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma, but the transcriptional programmes accompanying this transition have mostly been characterised in bulk tissue or in tumour tissue alone. We profiled paired samples spanning the full sequence by single-nucleus RNA sequencing (413,697 nuclei, 23 patients) and spatial transcriptomics (56 Visium sections, 25 patients), asking how the transcriptional state of the tissue changes as invasion proceeds and whether that state can be reversed in silico by known perturbations. The atlas resolves into 39 second-level subtypes and the tissue into seven recurring niche-domain archetypes (K* = 7), six of which are ordinary lung structures and only one of which is restricted to invasive disease. A confound then shaped the remaining analyses: domain identity, marker stability and the apparent stage-dependence of the spatial programmes all scale with sequencing depth, and depth is in these sections a proxy for tissue density rather than a purely technical artefact (rho=-0.89 between a domain's depth and the fraction of its markers surviving depth matching). Several apparently positive results disappeared once depth was matched. The invasive domain's ECM/interstitial identity survives that check: within patients the fibroblast ECM programme is higher in invasive disease in 20 of 22 paired cases (P=0.0033 against random gene sets), and the result is unchanged when the genes added by hand are removed and under every leave-one-out variant. Querying a patient-paired disease axis against the LINCS compound library with three scoring schemes returned 34 compounds in common, dominated by PI3K/mTOR inhibitors, with a split-half reproducibility of rho=0.962 against a permutation null of +/-0.045 and an agreement of rho=0.735 between a single-cell-derived and a spatially derived axis. A positive control, the recovery of known regulators of an official benchmark (TBXT ranked 1st of 8,450), establishes that the pipeline recovers true positives, so the negative results reported here are properties of the data rather than of the method. Those negatives are stated explicitly: single-cell copy-number inference produced no usable malignant/non-malignant separator, the cohort-level spatial copy-number difference did not survive depth matching, a developmental trajectory ran opposite to that of the source study, gene-level prediction returned no signal on the pooled axis and only one perturbation survives compartment-wise gating, and a ligand-level analysis produced no usable pair.

Aging is a network-level phenomenon, with its hallmarks interacting through dense feedback loops across vastly different timescales. Decades of reductionist research have produced thousands of mechanistic models of narrow subsystems, but no straightforward way to integrate them into one comprehensive system. Consequently, whole-cell and multi-hallmark aging models remain rare, manually constructed, and relatively small, despite broad agreement on their importance. The emergence of autonomous research agents offers a way to distribute this modeling effort across humans and artificial intelligence and thereby greatly accelerate it, but only if the underlying substrate allows agents to readily compose and analyze models with built-in validation checks and modeling guidance, without extensive custom code. We present hallsim, a JAX-native compositional simulation framework designed as the mechanistic substrate for agent-orchestrated research. It provides composability, end-to-end differentiability, GPU execution, and automated analysis and model-selection tools that facilitate composite model construction and fine tuning. The framework makes it easier to propose a mechanistic hypothesis, integrate it into an existing composite, reparametrize it against observed data, and batch-test it across different initial conditions. We demonstrate the co-simulation and in silico perturbation of three independently published kinetic models spanning genomic instability, nutrient sensing, and proteostasis. The models are connected by four edges and calibrated against a public dataset. Additionally, we train a Neural ODE surrogate and compose it alongside the mechanistic modules in a hybrid composite, demonstrating that mechanistic and neural models can function as complementary components of the same system.

Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes - suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.

The balance between how much human tumors recapitulate fetal tissue programs versus lose adult tissue identity remains unresolved. I used audited vibe coding, a human-mediated, cross-model critique-and-refinement workflow, to re-analyze a public pan-cancer proteomic atlas. A primary large language model wrote and executed the analysis under scientific direction, while a separate model family audited the code, outputs and claims; findings were returned for correction across seven versioned releases. Among 229 tumor-adjacent pairs in seven organs, tumor-minus-adjacent proteomic change partially aligned with reverse fetal-to-adult maturation (organ-balanced cosine, 0.240; 95% interval, 0.138 to 0.335), with positive alignment in 189 of 229 patients (82.5%). The organ-balanced projection coefficient was 0.195 (95% interval, 0.069 to 0.244), indicating movement along only part of the developmental distance. Although reverse maturation overlapped adult-identity loss, a positive developmental component remained after identity loss entered first (0.203; 95% interval, 0.129 to 0.239). Suppression of adult-high proteins contributed to more positive alignment than reactivation of fetal-high proteins. The vibe coding audits identified substantive defects. A common-mask correction reduced the matched-organ advantage from 0.074 to 0.059; a missing-value correction barely changed aggregate geometry but replaced 5 of the top 40 liver contributors; and coupled resampling repaired uncertainty accounting without changing patient scores. As with any single report, the "vibe reanalysis" biological results are candidate discoveries pending independent replication. The workflow is a single feasibility case, not a reliability benchmark, and shows how conversationally generated analysis can be made more inspectable when model-written code is treated as untrusted, versioned and subject to separate-model critique and executable checks.

Large language model (LLM) agents can propose combination therapies and construct supporting mechanistic models far quicker than either can be verified. To address this gap, we built a gated agentic AI quantitative systems pharmacology (Ai QSP) workflow where no hypothesis reaches a report until it clears strict hurdles for precedent, evidence, structural integrity, and release. We applied this pipeline end to end to healthy-ageing interventions. It began with a 34 state model calibrated on clinical trial data for semaglutide and metformin. Locked and evaluated against an independent post hoc DNA methylation trial, the model captured the direction of change across all 9 organ and system clocks, but the magnitude was far too small: it predicted about 0.14 years per year where 1.6 were observed, halting tier promotion. The workflow then generated seven nutritional combination hypotheses (H1H7) with 48 new parameters encoded in SBML. Identifiability analysis showed that 37 of these were completely unconstrained by available data. Trimming the model down to its lead pairing - a food derived bioactive (Agent A) plus a single-strain probiotic (Agent B left six identifiable parameters and locked the uncalibrated interaction term at zero. From this reduced model, we derived an 84 day trial with a 28 day washout, requiring 356 participants for 80% power at an interaction ratio of 0.75. A 9,000 person virtual trial confirmed the design (80.5% power), but revealed an operational trap: an assay floor at 24 mg/kg slashed power to 33% and skewed estimates toward the null, forcing a protocol-level censoring condition before study lock. Testing the pipeline across downstream operational stages and comparative oncology programmes showed that unconstrained generation consistently over-reports novelty, topological entry points can masquerade as biological mechanism, and governance gates remain vulnerable to operator overrides. Ultimately, the workflow delivered no silver bullet: its output is a single testable interaction, an assay hardened trial design, and an audit trail documenting why the other six hypotheses were shelved. Agent identities, doses, and agent specific sources are withheld in this version pending patent filing.

The limited treatment options available for the estimated 38% of adults worldwide affected by Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) are largely due to an incomplete understanding of the complex molecular networks underlying disease pathogenesis. To dissect the genetic architecture and proteomic regulation underlying MASLD, we generated a genetically diverse mouse cohort through a four-way cross of founder strains with varying susceptibility to liver disease, producing 444 F2 mice with a spectrum of phenotypes and genotypes. Utilizing deep proteomic profiling of the livers of this population, we identified quantitative trait loci (QTL) for 2,652 proteins, spanning over 4,000 unique genomic loci, and distinguished cis- and trans-acting regulatory mechanisms. Integrating proteomic, genomic, and phenotypic data reveals key regulatory loci and candidate proteins influencing disease progression, creating a mineable proteogenomic resource for MASLD research. We utilize this resource to identify the E3 ubiquitin ligase Ubr1 as a candidate central regulator connecting proteostasis and lipid metabolism, with genetic polymorphisms that may alter its abundance and predispose to metabolic dysfunction. This study demonstrates how high-throughput, deep proteome profiling integrated with QTL mapping can reveal complex gene-protein networks governing MASLD susceptibility and progression, offering novel insights for biomarker discovery and therapeutic targeting.

Decades of antibody-based protein profiling have generated valuable cohorts across evolving cytometry, sequencing and spatial imaging platforms. Differences in marker coverage, signal scale and antibody performance hinder joint analysis and reuse of cohorts with long-term clinical follow-up. Widely used RNA-based integration methods prioritize shared embeddings rather than directly comparable protein measurements. To fill in the gap, we developed RAMP (Rank-preserving Alignment of Multi-platform Proteomics), an unsupervised framework that aligns shared proteins without cell-type annotations. It combines sample-specific anchoring and cross-platform cell matching with bounded monotone transformations. The corrected values retain each protein marker's expression order across cells within a sample. Across seven integration tasks, RAMP achieved the strongest overall balance of batch correction and cell-type preservation. RAMP also improved marker-threshold transfer while preventing expression-order reversals observed with some comparison methods. These reversals made cell types with originally high marker expression appear lower-expressing than other populations. Such overcorrection can mislead cell identification and protein comparisons, even when the integrated embedding shows improved dataset mixing. By preserving each marker's expression order within a sample, RAMP protects the relationships needed to interpret normalized protein measurements. To investigate whether better harmonized inputs improve downstream learning, we used linear, nearest-neighbor and small neural-network models as a controlled, small-scale testbed. This approach tests an input quality question relevant to proteomic foundation models without undertaking large-scale pretraining. Under matched supervision, RAMP improved cross-platform annotation across all three model families and improved missing-marker prediction over unaligned inputs. These gains demonstrate the value of harmonized inputs for predictive learning and motivate evaluating RAMP as preprocessing for proteomic foundation models. In bone marrow, we used high-quality single-cell protein measurements to impute the failed CODEX CD19 channel, recovering B-cell spatial distributions. The completed panel improved B-cell separation from natural killer cells in the joint embedding. In melanoma, a proliferating CD8 T-cell state near tumor cells was associated with longer survival. Transfer such a spatial information to CITE-seq connected tumor proliferation and infiltration state to an adhesion and activation program involving PD-1, LFA-1 and CD2. In lung cancer, we used single-cell CyTOF measurements to predict unmeasured HLA-ABC states in vascular cancer-associated fibroblasts profiled by spatial imaging mass cytometry (IMC). Higher predicted scores were associated with lower immune-cell fractions and worse disease-free survival in the IMC cohort. In head and neck cancer, transferring a fibroblast/macrophage spatial niche score to CITE-seq identified a CD39/TIGIT-associated CD8 T-cell phenotype. Protein and RNA profiles supported checkpoint-associated features, and transfer back to CODEX showed correspondence with measured LAG3. RAMP enables existing single-cell and spatial cohorts to support new discoveries by connecting molecular programs, tissue organization and clinical outcomes across platforms.