Biology subjects

Merchant, A.

Publications and source records attributed to Merchant, A..

16 recordsLinked to original sources

bioRxiv2026

Efficient biological design via genomic in-context learning

In-context learning (ICL), the ability to perform new tasks from a few examples supplied in a prompt, is a defining emergent capability of large language models. Here, we show that ICL emerges in the genome language model Evo 2 and enables biological design. With as few as four examples in the prompt and no additional training, Evo 2 generates realistic metal-binding proteins, de novo antibodies, protein-RNA systems, and entire bacteriophage genomes. For {Phi}X174-like bacteriophage design, ICL surpasses previously fine-tuned models on computational measures of design quality and diversity. Beyond {Phi}X174, conditioning the model with different phage prompts enables us to efficiently design MS2 and M13 genomes in silico without fine-tuning. Iteratively updating the prompt with newly generated phage genomes, which we refer to as genomic chain-of-thought, further improves the diversity of designs while maintaining quality metrics. Paired with an inference engine that accelerates generation up to 35-fold and extends practicable context to a megabase, these results establish ICL as a versatile and efficient paradigm for biological sequence design.

bioinformatics↗

bioRxiv2026

Fine-Grained and Reliable Cell Classification from Pathology Images

Molecular assays such as spatial transcriptomics facilitate fine-grained identification of cell types for analyzing tumor microenvironments. However, they are expensive to acquire in routine clinical practice compared to hematoxylin and eosin (H&E) images. With molecular guidance, such images can distinguish cell lineages, yet often fail to resolve closely related subtypes. To overcome this, we introduce CellBridge, a multi-modal contrastive learning framework pretrained on 16M cells with paired H&E and spatial profiles, for transferring molecular information into morphological representations. CellBridge further incorporates a Cell-Ontology-aware conformal prediction module that assigns labels at different levels of specificity: leaf-level cell types when morphology alone provides sufficient evidence, and broader ancestor categories when distinguishing among more specific cell types would require molecular information. Across in-domain and out-of-domain benchmarks, CellBridge outperforms leading pathology foundation models in macro F1 by up to 16 percentage points, with the largest gains on the most fine-grained label spaces. It consistently achieves smaller prediction sets, while reducing class-wise coverage gaps. We envision CellBridge as a step toward extracting cellular information that currently requires costly molecular assays from routinely acquired pathology images, while explicitly identifying where such assays remain necessary. Our code is available at https://github.com/Lotfollahi-lab/cellbridge.

bioinformatics↗

bioRxiv2026

Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization

Spatially-resolved transcriptomics (SRT) measures gene expression at single-cell resolution while preserving each cells spatial location, enabling the joint study of cell identity and cellular niche, the recurring microenvironment that organises tissue function. Existing representation-learning methods typically capture only one of these axes at a time. We present SQUINT, a graph vector-quantized variational autoencoder (VQ-VAE) that learns two disjoint codebooks per cell from a shared architecture: a cell codebook quantising the per-cell embedding before neighbourhood aggregation, biased toward cell-intrinsic identity, and a niche codebook quantising the embedding after graph neural network (GNN) aggregation, biased toward spatial context. Both use residual vector quantization, giving a coarse-to-fine discrete-token hierarchy. SQUINT is trained with per-branch negative-binomial reconstruction objectives and three domain-motivated components that we show are crucial: a within-section cosine adjacency loss that anchors the niche codes in the spatial graph, a cross-section contrastive loss on the cell latents that aligns transcriptomically matched cells, and a decoder section covariate that absorbs batch effects. Across three datasets spanning four spatial assays (STARmap, MERFISH, CosMx, Xenium) and four tasks - niche identification, cell-type identification, cross-section integration, and spatial gene-expression imputation in held-out regions - SQUINT outperforms or is competitive with strong baselines on identification and achieves the most faithful cross-section integration. The resulting discrete vocabulary makes tissues directly consumable by transformer-style foundation models and enables one-step query-to-reference atlas mapping via code-distribution similarity, which we demonstrate on a CosMx human non-small-cell lung cancer cohort.

genomics↗

bioRxiv2026

Multi-scale modeling of human tissues from spatial transcriptomics with TERRA

Spatial transcriptomics maps gene expression at cellular resolution, revealing how cells organize into multicellular niches. Yet computational analyses remain dataset-specific, without a transferable representation of tissue organization that generalizes across datasets, tasks and tissues or predicts how tissues behave under perturbation. We present TERRA, a foundation model pretrained on 112 million human cells profiled by spatial transcriptomics. From a single pretrained backbone, TERRA yields embeddings at the scale of cells, the genes they express and the neighborhoods in which they reside, and supports spatial in silico perturbation, all applied zero-shot to unseen tissues. At the cell level, in newly generated spatial data for developing pancreas, TERRA identified an islet-associated capillary state which we posit represents a developmental precursor of the mature islet microvasculature. At the gene level, in untreated kidney sections, in silico knockout of immune-checkpoint targets predicted a gene program of immune-checkpoint-blockade-associated nephrotoxicity, which we validated in treatment-exposed tissue and recovered in blood. At the neighborhood level, TERRA mapped macrophages across tissues to identify recurring cross-organ niches, which we term archetypes, including a tumor-boundary niche associated with poor prognosis in kidney cancer. Together, TERRA captures the spatial and multicellular logic of human tissue and predicts, in silico, its response to perturbation, providing a multi-scale framework for tissue biology, therapeutic development and clinical application.

genomics↗

bioRxiv2026

A high-level programming language for generative biology with Proto

Programmable composition of complex systems is a longstanding goal of biological research. Generative modeling has improved the reliability of computational design, but existing methods are highly specialized and are difficult to extend or compose. Here, we introduce Proto, a high-level programming language for generative biology. By composing a small set of abstract primitives into structured programs, Proto encodes generative design campaigns across diverse modalities and scales--spanning DNA, RNA, proteins, ligands, and their interactions. Proto readily incorporates predictive models into generative workflows, which we leveraged to design alternatively spliced introns with experimental validation in human cell lines. Proto is natively multi-objective, enabling the design of promoter-repressor pairs with leading experimental success rates for synthetic protein-DNA design. Alongside AI agents, Proto enables the specification of complex pathways and regulatory logic through natural language instructions. We openly release Proto, including software infrastructure and user interfaces, to enable widespread access to generative biological programming.

Synthetic Biology↗

bioRxiv2026

Field-based dissection of stomatal anatomy and conductance reveals stable QTL under drought and heat in wheat

Stomatal traits balance carbon gain with water loss, yet their breeding potential in wheat remains underexploited. This study investigated physiological and anatomical stomatal responses alongside yield across two years of large-scale field trials under water-limitation and delayed sowing-induced heat exposure. Across both seasons, stomatal conductance (gs) declined under stress, reflecting strong environmental constraint on gas-exchange (water-limitation:-26.9%; heat:-13.8%). Partitioning responses by leaf surface and genotype identified the adaxial surface as the dominant contributor to gs variation and the most stress responsive. Despite increases in theoretical anatomical gas-exchange capacity (gsmax), gs-efficiency declined, indicating partial decoupling between structural potential and realised conductance. Drought reduced stomatal size while increasing density whereas heat increased size, suggesting stress-specific anatomical plasticity. Moderate-to-high heritability was observed for anatomical traits (Water-limitation: 0.13-0.57; Heat: 0.42-0.71), contrasting with lower and less stable heritability for gs (water-limitation: 0.13-0.41; heat: 0.13-0.50). Genome-wide-association-mapping identified 169 putative QTLs, predominantly for anatomical traits, including stable and co-localised pleiotropic loci. Fourteen sets of closely positioned markers were detected across seasons or studies, with stable regions on chromosomes 2B, 3B and 7B emerging as key loci. Focusing on stable loci controlling adaxial stomatal anatomy offers a realistic strategy to enhance wheat photosynthetic efficiency and climate resilience. HighlightAdaxial stomatal traits dominate gas exchange responses to heat and drought in wheat, with stable anatomical QTL identified on chromosomes 2B, 3B and 7B. Their stability across environments supports their relevance for crop improvement in water-limited and high temperature systems.

physiology↗

bioRxiv2026

Self-supervised learning for a gene program-centric view of cell states

Single-cell omics has extended the biological interrogation of cell state from examining the expression of individual genes to unbiased profiling of tens of thousands of genes at once. However, extracting biological insights from such high-dimensional data remains challenging. To enable downstream analyses, many computational approaches compress cell state into a single latent representation. This can obscure the structure of underlying gene programs (GP), defined as coordinated sets of biologically related genes, such as signalling pathway response modules or transcription factor targets. Here, we present Tripso, a self-supervised transformer deep learning model which learns multiple GP-specific embeddings from predefined GPs, while also enabling the discovery of novel, data-driven GPs. Tripso facilitates principled comparisons across development, disease, and experimental systems. Firstly, in a dataset of human hematopoietic cells spanning prenatal development through adulthood and aging and including newly generated data, Tripso resolved age-specific GP patterns, including elevated JAK-STAT activity in pediatric hematopoietic cells and postnatal shifts in IKZF1 GP activity during B cell differentiation. Secondly, leveraging Tripso GP embeddings and comparing in vivo to in vitro data, we hypothesized and experimentally validated that inhibition of the SEC61 translocon improved maintenance of hematopoietic stem cells in culture. Finally, Tripsos capacity for data-driven GP discovery revealed a previously uncharacterized tissue-resident memory T cell GP with increased activity in atopic dermatitis. Its spatial co-localization with sebaceous gland-associated immune niches was demonstrated in spatial transcriptomic and proteomic data. Thus, by moving beyond single embeddings of cellular states, Tripso enables interpretable and actionable discoveries, demonstrating how GP-centric modelling can generate hypotheses with substantial biomedical relevance. By anchoring cellular representations in meaningful GPs, Tripso establishes a principled and biologically grounded framework towards the development of interpretable virtual cell models.

bioinformatics↗

bioRxiv2025

QTL for Heat-Induced Stomatal Anatomy Underpin Gas Exchange Variation in Field-Grown Wheat

Stomata are central to leaf gas exchange, governing carbon uptake, water loss, and ultimately, crop performance. However, the contribution of integrated stomatal anatomy and physiology to wheat heat tolerance remains poorly understood, particularly under realistic field conditions and across divers germplasm. This study explored the role of stomatal anatomical and physiological traits in shaping wheat responses to heat stress. Across two years of multi-environment field trials encompassing 200 genotypes in season 1 and 50 genotypes in season 2, we examined stomatal conductance (g), anatomical traits including stomatal size and density, and the stomatal conductance operating efficiency (gse) across leaf surfaces, along with grain yield. Timely and delayed sowing treatments were used to expose key developmental stages (anthesis) to contrasting temperature regimes. Early sowing supported higher gs and gse, while delayed sowing impaired stomatal function despite similar theoretical anatomical capacity (gsmax), revealing a decoupling of structural potential and physiological performance under stress. The adaxial surface consistently exhibited higher gs, stomatal density, and gsmax than the abaxial surface, highlighting its dominant role in leaf gas exchange. Later sowing induced plastic shifts in anatomy, including smaller, denser stomata, particularly on the adaxial surface, suggesting an adaptive response to thermal stress. Significant genotypic variation was observed for gs, gse, gsmax, and stomatal anatomical traits, with moderate heritability indicating genetic control. 125 putative QTL were identified for multiple stomatal traits across environments, including several stable loci on chromosomes 2B and 5B, and numerous closely clustered QTL for anatomical traits on chromosome 7B, highlighting key genomic regions underlying stomatal anatomy. In contrast, QTL for gs and gse were fewer and season-specific, highlighting the environmentally sensitive nature of physiological stomatal regulation. 42 of the QTL identified were consistent with previously reported QTL for stomatal traits in wheat. Together, these findings elevate the role of stomatal traits, supporting an integrated breeding strategy that combines selection for favourable stomatal anatomy with efficiency physiological regulation. Incorporating traits like gse into selection frameworks may enhance yield stability and resilience in heat-prone environments, advancing the development of climate-resilient wheat ideotypes. ScopeThis manuscript examines how stomatal anatomical and physiological traits integrate to shape wheat responses to heat stress under field conditions, addressing a central challenge in understanding the roles of stomata in a warming climate. Using multi-environment field trials across two growing seasons and encompassing 200 wheat genotypes, we quantify stomatal conductance (gs), anatomical traits, and stomatal operating efficiency (gse), across adaxial and abaxial leaf surfaces, and investigate whether these are under genetic control. The study provides mechanistic insight into the dynamic regulation of stomatal function by demonstrating a decoupling between anatomical capacity and physiological performance under heat stress, alongside plastic shifts in stomatal traits across sowing times. The identification of 125 candidate QTLs, including numerous stable and co-localised QTL across seasons, supports their incorporation into breeding programs aimed at enhancing resilience and yield stability. This work aligns with the Research Topic by elucidating mechanistic underpinnings of stomatal conductance regulation, bridging stomatal biology with applied crop improvement strategies. This work is critical to improving understanding of plant water relations and carbon uptake under future climate scenarios to ensure food security in a changing climate.

plant biology↗

bioRxiv2025

Functional genomics and tumor microenvironment analysis reveal prognostic biological subtypes in Mantle cell lymphoma

Mantle cell lymphoma (MCL) is a genetically and clinically heterogeneous B-cell malignancy. We studied two MCL cohorts with differing treatment patterns: one enriched for immunochemotherapy, the other for chemotherapy alone. TP53 alterations were consistently associated with poor prognosis, whereas ATM mutations correlated with improved outcomes following rituximab-based chemotherapy. Based on recurrent genetic events, six clusters were identified and refined into three prognostic groups: high-risk (TP53 mutations and deletions at 17p13.3, 13q14.2, and 19p13.3), intermediate-risk (ATM and epigenetic regulator mutations, or gains at 8q/17q/15q), and low-risk (lacking TP53 alterations, rare ATM mutations without 11q deletions, gains at 3q, deletions at 6q). Transcriptomic analysis revealed enrichment of proliferation, metabolism-promoting gene signatures in high-risk; angiogenesis and NOTCH signaling in intermediate-risk; and proinflammatory-related (i.e., IFN, TNF) in low-risk MCLs. Multi-proteomic spatial profiling using imaging mass cytometry (IMC) demonstrated enrichment of CD8 T cells with high expression of exhaustion markers and dominant population of myeloid cells skewed toward an M2-like phenotype. Compared to ATM-perturbed tumors, TP53-perturbed tumors exhibited enriched SOX11 tumor cells and enhanced tumor-immune cell interactions. Functional analysis revealed that p53 represses BCR signaling through PTPN6 activation. Collectively, these findings highlight distinct molecular and immune landscapes and reveal therapeutic vulnerabilities in high-risk TP53-altered MCL.

cancer biology↗

bioRxiv2025

Multidimensional characterization of cellular ecosystems in Hodgkin lymphoma

The tissue architecture of classic Hodgkin Lymphoma (CHL) is unique among cancers and characterized by rare malignant Hodgkin and Reed-Sternberg cells that co-evolve with a complex ecosystem of immune cells in the tumor microenvironment (TME). The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. Here, we employed an integrative, multimodal approach to characterize CHL tumors using malignant cell sequencing, spatial transcriptomics and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P), each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture involving immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of the CST subtype, revealed dysregulated oncogenic signaling and unique TME crosstalk. These findings highlight the significance of multi-dimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities.

cancer biology↗

bioRxiv2024

FieldDino: High-throughput physio-morphological phenotyping of stomatal characteristics for plant breeding research

Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high-throughput field-based phenotyping approach, FieldDino, for screening of stomatal physiology and anatomy. The method allows coupled measurements to be collected in <15 s and consists of: (1) stomatal conductance measurements using a handheld porometer; (2) in situ collection of epidermal images with a digital microscope, 3D-printed leaf clip and Python-based app; and (3) automated deep learning analysis of stomatal features. The YOLOv8-M model trained on images collected in the field achieved strong performance metrics with an mAP@0.5 of 97.1% for stomatal detection. Validation in large field trials of 200 wheat genotypes with two irrigation treatments captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress. HighlightChaplin et al., have developed FieldDino which enables rapid, high-throughput phenotyping of stomatal traits, advancing plant breeding research by integrating streamlined in-field measurements with automated deep learning analysis.

physiology↗

bioRxiv2023

Imaging Mass Cytometry-Driven Spatial Analysis and Biomarker Discovery in Hodgkin Lymphoma

The biology of tumors is suffused with spatial interactions, such as tumor-immune signaling through localized cytokine/ligand secretion, cell-cell contacts, and checkpoint ligand/receptor signaling. Hodgkin Lymphoma (HL) can serve as a study paradigm for tumor microenvironment (TME) architecture as the defining pathological feature is the scarcity of the malignant Hodgkin and Reed Sternberg (HRS) cells, leaving a diverse and predominantly immune cell rich tumor microenvironment (TME) with complex tumor-immune interactions. Previous studies have identified TME features that are prognostic and predictive, however these studies did not consider the entirety of TME cellular ecosystems, including precisely defined immune cell subsets with opposing inflammatory and immune-suppressive effects, as a determinant for differential clinical course of HL patients. Here we use Imaging Mass Cytometry (IMC) with 42 antibody markers to profile tumors from 93 patients with HL. Our cohort consists of relapsed/refractory HL with matched diagnostic and relapsed biopsies, and we present a bioinformatic pipeline to profile 10 major cell lineages and their subtypes including spatial interaction mapping. Our pipeline identifies putative biomarker candidates with a focus on "rosettes" - local aggregates of immune cells around single tumor cells. In addition to validating existing biomarkers centered on CD68+ macrophages, GranzymeB+CD8+ T cells, and others in HL, we propose new biomarkers based on localized interactions between HRS cells and aggregating CD4+ and CD8+ T cells and macrophages involving the immune checkpoints PD1/PDL1, LAG3, and Galectin9. This study serves as a broad tissue imaging resource for multi-timepoint biopsies in HL, and a computational resource and pipeline for users of IMC and other multiplexed imaging studies to perform tissue analysis and biomarker candidate testing with any tissue type.

cancer biology↗

bioRxiv2023

The spatially resolved tumor microenvironment predicts treatment outcome in relapsed/refractory Hodgkin lymphoma

PURPOSEAbout a third of relapsed or refractory classic Hodgkin lymphoma (r/r CHL) patients succumb to their disease after high-dose chemotherapy followed by autologous stem cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL. METHODSWe performed imaging mass cytometry (IMC) on 169 paired primary diagnostic and relapse biopsies using a marker panel specific for CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes. RESULTSHighly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early relapse/refractory CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single cell RNA sequencing data identified unique architecture defined by CXCR5+ HRS cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay ( RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk vs low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay. CONCLUSIONSWe identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

cancer biology↗

bioRxiv2022

Genomic loss of the HSP70cA gene in the vertebrate lineage

Metazoan 70 kDa heat shock protein (HSP70) genes have been classified into four lineages: cytosolic A (HSP70cA), cytosolic B (HSP70cB), endoplasmic reticulum (HSP70er), and mitochondria (HSP70m). Because previous studies have identified no HSP70cA genes in vertebrates, we hypothesized that this gene has lost on the evolutionary path to vertebrates. To test this hypothesis, the present study conducted a comprehensive database search followed by phylogenetic and synteny analyses. The HSP70cA gene was present in invertebrates and animals that belong to subphyla of Chordata, Cephalochordata (lancelets) and Tunicata (tunicates). However, the genomes of early vertebrates in the subphylum Craniata (lamprey, hagfish, elephant shark, and coelacanth) contained only HSP70cB genes, suggesting the loss of the HSP70cA gene in the early period of vertebrate evolution. Synteny analysis using available genomic resources indicated that the synteny around the HSP70 genes was generally conserved between tunicates, but it was largely different between tunicates and lamprey. These results suggest the presence of dynamic chromosomal rearrangement in early vertebrates, which possibly caused the loss of the HSP70cA gene in the vertebrate lineage.

evolutionary biology↗

bioRxiv2021

Vulnerability to cavitation is linked to home climate precipitation across eight eucalypt species

Vulnerability to cavitation in leaves is the result of highly adaptive anatomical and physiological traits that can be linked to water availability in a species climate of origin. Despite similar gross leaf morphology, eucalypt species are often confined to specific climate envelopes across the variable rainfall environments of Australia. In this study, we investigate how the progression of cavitation differs among eucalypts and whether this is related to other hydraulic and physical leaf traits. We used the Optical Visualisation technique to capture cavitation progression across the leaves of eight eucalypt species (Angophora crassifolia, Corymbia tessellaris, Eucalyptus atrata, Eucalyptus grandis, Eucalyptus laevopinea, Eucalyptus longifolia, Eucalyptus macrandra, Eucalyptus tereticornis) from a wide range of climates and grown in a common garden setting. Vulnerability to cavitation, represented by the leaf water potential required for 50% cavitation of leaf vessels, varied significantly among species (-3.48 MPa to -8.25 MPa) and correlated linearly with home climate precipitation and leaf SLA (R2 of 0.64 and 0.75, respectively). P12-P88, the range of water potentials between which 12% to 88% of cavitation occurs, was decoupled from P50 but also correlated with leaf SLA (R2 of 0.72). We suggest the magnitude of P12-P88 may be representative of a species drought strategy - a large P12-P88 signifying leaves that exhibit drought tolerance (retention of leaves under drought conditions) and a small P12-P88 signifying drought avoidance (leaf shedding after a threshold of drought is reached). Our results agree with other studies that highlight these cavitation metrics as genetically fixed traits. Turgor loss point, on the other hand, may be more plastic, as evidenced by the low variability of this trait across these eucalypt species grown in a common garden environment. Further study will help to establish the SLA-related anatomical traits that impart cavitation resistance and to extend these conclusions to a greater number of species and home climates.

plant biology↗

bioRxiv2018

Rate of photosynthetic acclimation to fluctuating light varies widely among genotypes of wheat

HighlightSignificant variation exists in the acclimation time of photosynthesis following dark-to-light transitions across wheat genotypes, under field and controlled conditions. Slow acclimation reduced daily carbon assimilation by up to 16%.\n\nAbstractCrop photosynthesis and yield are limited by slow photosynthetic induction in sunflecks. We quantified variation in induction kinetics across diverse genotypes of wheat for the first time. In a preliminary study using penultimate leaves of 58 genotypes grown in the field, we measured induction kinetics for maximum assimilation rate (Amax) after a shift from full darkness to saturating light (1700 mol m-2 s-1) with 1-4 replicates per genotype. We then grew 10 of these genotypes with contrasting responses in a controlled environment and quantified induction kinetics of carboxylation capacity (Vcmax) from dynamic A vs ci curves after a shift from low to high light (50 to 1500 mol m-2 s-1), with 5 replicates per genotype. Within-genotype median time for 95% induction (t95) varied from 8.4 to 23.7 min across genotypes for Amax in field-grown penultimate leaves, and from 6.7 to 10.4 min for Vcmaxin chamber-grown flag leaves. Our simulations suggested that non-instantaneous acclimation reduces daily net carbon gain by up to 16%, and that breeding to speed up Vcmax induction in the slowest genotype to match that in the fastest genotype could increase daily net carbon gain by more than 4%, particularly for leaves that experience predominantly short-duration sunflecks.

physiology↗