Nature Photonics

Solid-state nanopores offer label-free, real-time single-molecule sensing. However, resolving fast biomolecular transport requires high-bandwidth data acquisition while the intrinsic high-frequency noise limits recovery of informative events. Here we present a hardware-software co-designed nanopore sensing platform that combines wafer-scale low-noise device engineering with deep learning-based signal reconstruction. A low-dielectric SU8 coating on silicon nitride nanopores reduces device capacitance to the pF range and suppresses high-frequency noise by up to 5-fold while maintaining facile, controllable and reproducible fabrication. This extends usable acquisition to 40 MHz and enables capture of fast molecular features. Coupled with a reconstruction model trained on synthetic translocation events embedded in experimentally measured noise, the platform recovers transient sublevels while preserving blockage edges and temporal fidelity. Using engineered DNA molecules carrying dumbbell-like barcodes, we resolve nanometer-scale structural spacings on sub-microsecond timescales, and experimentally quantify translocation dynamics within the sub-10 nanometer regime. Dual-channel measurement on a single nanopore device further demonstrates transferability of the platform by showing robust cross-channel signal reconstruction across distinct baseline noise levels. Our approach provides a general route for reliable recovery of fast event features and may enable more information-rich single-molecule sensing across diverse biomolecular targets.

Molecular information is vital for imaging technology. Optical imaging acquires molecular specificity almost exclusively via labeling strategy, which is fundamentally constrained by limited multiplexing capacity, high running costs, and experimental complexity. Conversely, label-free optical imaging offers substantial technical simplicity but is believed to have little true molecular specificity. Contrary to common belief, here we introduce super-multiplex optical imaging without labeling. By systematically studying paired vibrational spectroscopic imaging and mass spectrometry imaging, we discovered a surprisingly strong (more than 0.9) correlation between their latent space representations, supported by both experiments and theory. This insight prompts us to build supervised learning models to successfully predict spatial distribution of 100 molecular species directly from label-free vibrational images across diverse tissue systems. We developed this technology, named Prediction through Learning with AdvaNced Chemical Kaleidoscope (PLANCK), and demonstrated it with both infrared-based vibrational imaging of organ-scale tissues and Raman-based vibrational imaging of live tissues. Powered by AI, PLANCK decodes the exquisitely rich but otherwise hidden vibrational information into a surprisingly large number of ([≥]100) specific molecular species, providing a cost-effective and scalable solution for basic research and translation, including applications in live imaging.

Coherent Raman spectroscopy enables label-free biochemical fingerprinting of live cells with subcellular resolution. We previously developed a machine learning framework capable of classifying glioma FFPE tissues using Raman spectral signatures. To accelerate live cell acquisition, we previously developed RADAR (Raman Spectral Analysis Using Deep Learning for Artifact Removal), a method that increases imaging speed by an order of magnitude while preserving spectral integrity. By integrating high-speed Raman imaging with supervised machine learning, we aimed to define unique biochemical fingerprints specific to cell type. We hypothesized that intrinsic biochemical composition alone is sufficient to distinguish cellular identity and tumor subtype. To test this, we generated metabolic maps of diverse brain-derived cell types--including astrocytoma, oligodendroglioma, and glioblastoma cells--using coherent Raman spectroscopy at single-cell resolution. Patient-derived brain tumor cell lines representing genetically heterogeneous backgrounds were analyzed. Samples were stratified by IDH1 mutation status (IDH1-mutant and IDH1-wild-type) and histologically classified as oligodendroglioma or astrocytoma. Raman spectral data were acquired from 286 live single cells across the two principal molecular classes, with further subdivision into two histologic subtypes within the IDH1-mutant group. Classification was performed using an XGBoost model with shallow tree depth (1-3), a 20% held-out test set, and grouped, stratified 5-fold cross-validation to control for sample-level bias. The machine learning framework distinguished IDH1-mutant from IDH1-wild-type cells with a ROC-AUC of 0.78 and further discriminated IDH1-mutant astrocytoma from oligodendroglioma cells with a ROC-AUC of 0.81. Feature importance analysis demonstrated that separation between IDH1-mutant and IDH1-wild-type cells was driven primarily by Raman peaks associated with protein amide bands, total NADH, unsaturated fatty acids, and heme-related vibrational modes. Within the IDH1-mutant class, discrimination between oligodendroglioma and astrocytoma was driven by lipid-rich vesicle signatures, protein/polyamide amide bands, and lipid-associated spectral features. Together, these findings support the feasibility of label-free, machine learning-assisted Raman profiling to resolve clinically relevant glioma subtypes at single-cell resolution. This scalable analytical framework provides a translational platform for investigating metabolic heterogeneity, therapeutic response, co-culture systems, and patient-derived organoid models.

Recent work has shown that genetically engineered proteins can serve as quantum bits in living systems. These quantum bits arise from the photochemistry of protein-bound flavins: blue-light excitation drives electron transfer to form a spin-correlated radical pair whose coherent singlet-triplet interconversion makes the protein's fluorescence sensitive to weak magnetic fields. Because this radical-pair reaction depends on the redox state of the flavin, itself a central electron carrier in cellular metabolism, the magneto-fluorescence of a biological qubit is intrinsically coupled to the biochemistry around it. This suggests a powerful application of fundamental significance in biology, until now an unsolved problem in the field of quantum sensing. Here we show a new class of quantum sensor, mtMagLOV2, that interfaces directly to a defining feature of life itself: the bioenergetic state of the cell. We genetically engineer flavin mononucleotide (FMN)-containing, magnetic-field-sensitive fluorescent proteins (biological qubits) to be expressed and translocated into the key bioenergetic machinery of the cell: the mitochondrial matrix. Using confocal and super-resolution microscopy, mtMagLOV2 localizes to the mitochondrial cristae, home of the electron transport chain complexes I-V and ATP synthase, the site of oxidative phosphorylation (OXPHOS). By pharmacological manipulation of OXPHOS, we show that the sensor's magneto-fluorescence tracks the redox (oxidation, reduction) state of the mitochondrial flavins, providing a quantum readout of redox status. The response differs between cancer cells (which rely heavily on glycolysis) and cardiomyocytes (which rely predominantly on OXPHOS), demonstrating quantum bioenergetic profiling. Together, these results establish biological qubits as quantum sensors capable of probing mitochondrial bioenergetics, opening a quantum window into the energetic machinery of living cells. More broadly, we anticipate that coupling quantum redox sensitivity to the specific biochemical targets will extend the reach of quantum technologies across the life sciences.

Microtubules (MTs) and their motor proteins collectively harness chemical energy to generate mechanical work, driving some of the most coordinated self-organized dynamics in living cells. The unique properties of these molecules also make them versatile building blocks of cytoskeletal active matter and biomimetic nanomachines that recapitulate cellular motility, emergent pattern formation, and motor-driven transport. However, these canonical systems use MTs of fixed length and do not incorporate the natural ability of MTs to grow and regenerate. Here, we go beyond these limits by using dynamic self-amplifying branched MT networks. Driven by kinesin-1 and cytoplasmic dynein activity, surface-gliding branched MT bundles undergo swarming that yields large-scale collective MT architectures with several sought-after features. They are polar and orientationally aligned, dense, span millimeter scales, and persist over hours. We then show that these features enable molecular transport along the swarm at unprecedented capacities, with up to six million motor complexes walking in parallel across millimeter-scale distances over hours. Our results introduce a new regime in cytoskeletal active matter in which the interplay between motor-driven activity and filament generation via branching leads to emergent polar order in proliferating swarms. Such emergent polarity makes these swarms suitable for engineering scalable transport nanotechnologies and programmable soft materials.

Although microplastics and nanoplastics (MP/NP) are pervasive environmental contaminants, our understanding of cellular toxicity remains incomplete, as adverse effects are often attributed to long-term intracellular accumulation, while the spatiotemporal onset of cellular damage remains poorly defined. Here, we employ chemical-bond-selective stimulated Raman scattering (SRS) microscopy and cell models that decouple continuous exposure from intracellular retention to directly visualize clinically derived MP/NP-cell interactions. Cellular stress occurs primarily during MP/NP exposure, accompanied by alterations in lipid droplet (LD) composition. In contrast, following extracellular removal, intracellularly retained MP/NP become largely inert, with recovery of lipid metabolism and cellular functions. Lipidomics identifies arachidonic acid (AA) as a key dysregulated metabolite, and SRS imaging further reveals transient, spatially confined AA enrichment in MP/NP-proximal LDs during uptake. Importantly, phospholipid coating of MP/NP attenuates LD alterations and cytotoxicity while preserving particle internalization, establishing uptake-driven metabolic stress, rather than long-term intracellular retention, as primary source of MP/NP-induced damage.

Scanning ion conductance microscopy (SICM) provides gentle, non-contact cell surface imaging, but it has not been used to investigate intracellular structures because the plasma membrane restricts nanopipette access. Here, we combined SICM with microsonication-based cell de-roofing to expose intracellular actin stress fibers (SFs) in U2OS cells for nanotopographical and -mechanical characterization. Importantly, the de-roofing conditions preserved actomyosin contractility, allowing analysis of SF structural and biomechanical changes during ATP-induced contraction. Resting SFs displayed an average height of 203{+/-}38 nm and width of 357{+/-}73 nm, and a complex surface architecture characterized by regularly spaced long-range height modulations (~500 nm periodicity; Wq ~25 nm) and smaller irregular corrugations (Ra ~19.2 nm). ATP stimulation reduced SF height and width by ~39% and ~15%, respectively, while largely preserving surface corrugation patterns. During contraction, some SFs separated into two longitudinal strands. High-resolution SICM imaging also revealed filamentous crosslinks mechanically coupling neighboring SFs, and nanomechanical measurements demonstrated local stiffening during contraction. These findings provide new insight into the structural and mechanical regulation of SF contraction and highlight the potential of SICM combined with cell de-roofing as a powerful platform for studying dynamic intracellular processes at nanometer resolution.

Programmable molecular biology increasingly requires strategies for converting engineered recognition or proximity modules into measurable outputs, particularly within transcriptional regulation, RNA imaging, and CRISPR-associated systems. Synthetic chemically induced dimerization (CID) systems provide a class of programmable recognition modules for such applications, yet generalized strategies for coupling structurally diverse CIDs to functional readouts remain limited. Here, we introduce a CID-to-output conversion strategy based on engineering of the linker-mediated coupling interface. Using single-fluorescent-protein sensors as an experimentally tractable optical model readout, we systematically varied paired N- and C-terminal linkers flanking circularly permuted green fluorescent protein (cpGFP) to map coupling landscapes across synthetic CID systems derived from combinatorial selection and computational protein design. The results revealed strong non-additive interactions across paired linkers and suggest that linker length is a first-order determinant of CID-to-output coupling. Across nanobody-, monobody-, and de novo-designed CID architectures, this framework yielded functional sensors with dynamic ranges up to 1270% and robust responses in mammalian cells. Together, this work demonstrates that effective CID-to-output conversion can be achieved by empirically mapping the linker-mediated coupling interface, providing a practical route for adapting synthetic CID to diverse programmable molecular readouts and nucleic-acid-associated synthetic biology systems O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/735888v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1111094org.highwire.dtl.DTLVardef@1579e8aorg.highwire.dtl.DTLVardef@16981feorg.highwire.dtl.DTLVardef@1d588f7_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cryo-electron microscopy (cryo-EM) of biological specimens is limited by radiation damage and a low signal-to-noise ratio (SNR). Here, we show that reducing the illuminated area substantially slows the observed diffraction decay in protein microcrystals. We further show that narrow parallel-beam electron diffraction from thin non-crystalline biological specimens provides substantially higher reciprocal-space SNR than conventional cryo-EM imaging. We developed a multimodal scanning workflow, 4D-para-STEM, that records narrow-beam diffraction patterns together with corresponding images. Using viruses, peptide assemblies, and microtubules, we demonstrate interpretable diffraction signals from both crystalline and non-crystalline biological specimens. Together, these results show that narrow parallel-beam scanning reduces observed radiation damage and improves the SNR in cryo-EM.

Single-molecule (SM) imaging (SMI)-based approaches have the powerful ability to capture receptor interactions, which are necessary for cell signaling, in their native live-cell environment. Yet, due to substoichiometric labeling, SMI generally provides only partial information on these interactions. We developed Deep-FISIK, which utilizes graph neural networks and multi-head attention for message-passing, to predict from SMI data the kinetics of homotypic interactions of the full receptor system. The input to Deep-FISIK are the SM detections in SMI experiments, without the need for explicit tracking. Thus, Deep-FISIK is compatible with labeling a higher fraction of receptors in the SMI experiments, increasing the prediction accuracy of the interaction kinetics parameters. The performance of Deep-FISIK is robust in the presence of a variety of deviations from the training data, indicating the applicability of Deep-FISIK to many receptor systems and SMI experiments.

In cells, cytoskeletal filaments such as microtubules are dissipative polymers that switch stochastically between growth and rapid collapse, a behaviour known as dynamic instability. This switching is coupled to nucleotide hydrolysis, so a filament's fate depends on the chemical state of its subunits and the free-monomer pool. Previously reported synthetic assemblies can be cycled between assembled and disassembled states, but the switch is typically set by the global fuel level rather than by a state stored within each monomer. Here we demonstrate a DNA/RNA hybrid polymer in which every monomer holds a one-bit internal state, assembly-competent or inactivated, flipped irreversibly by cleavage of an internal RNA linkage. The bit is written by two routes sharing the same transesterification chemistry: a slow spontaneous cleavage giving each monomer an intrinsic lifetime, and a fast, site-specific write by a programmable DNAzyme. Because inactivation is irreversible, sustained cycling requires continuous regeneration of active monomer, holding the system in a non-equilibrium steady state in which filaments undergo repeated depolymerization and rescue at frequencies near 0.2 (min)-1. We also find that the filaments form meshes auto-catalytically. Because each crosslink recruits filaments from the pool, crosslinking accelerates autocatalytically, driving a percolation transition to a system-spanning network that continuously remodels as its filaments turn over. Thus the timing of switching can be stored within individual monomers rather than imposed as a global threshold -providing a route to autonomously remodelling active materials.

Modelling human cortical microcircuitry in vitro requires platforms that recapitulate both the compositional complexity and spatial architecture of developing neural tissue. Current organoid and assembloid models often rely on the bulk fusion of pre-differentiated, region-specific cells, lacking the capacity for emergent spatial co-differentiation and microenvironment-driven multiscale organisation. There is also a lack of neural and neuronal-glial models with photo-architectured network geometries. To address these limitations, we present a volumetric in situ differentiation system using a triculture of precision reprogrammed human iPSC-derived glutamatergic neurons, GABAergic neurons and astrocytes embedded throughout ultra-soft photocrosslinkable hydrogel microenvironments. The deterministic and spatially controlled method allows us to engineer macro-scale, interconnected human neural networks directly onto functional microelectrode array interfaces using projection photopatterning for high-throughput screening. Unlike fusion-based organoids and assembloids, our platform enables simultaneous, spatially distributed lineage differentiation and maturation, and extensive topography-guided neurite outgrowth bridging localised cellular hubs to recapitulate various aspects of neurodevelopmental patterning and synaptic integration in 3D. The model enables topographic patterning of neuronal-glial networks as well as 3D cell-embedded bioprinting with the developed triculture system. Both modes of cellular growth are studied and demonstrated here. Longitudinal electrophysiological tracking over a month of culture reveals a transition from immature, quiescent states to asynchronous, information-dense microcircuits characterised by an expanded state-space manifold and physiological excitatory-inhibitory balance. By replicating the mechanics of native brain parenchyma, the model presents a highly reproducible, scalable and flexible platform for the study of cortical microcircuitry development, neurodegenerative decline, and inter-regional network assembly.

The nanoscale organization of cell-adhesive ligands is increasingly recognized as a determinant of cell behavior, yet whether it directly regulates cellular metabolism remains unclear. Here we show that supramolecular clustering of integrin-binding ligands regulates mitochondrial respiratory capacity through integrin-mediated mechanotransduction. Supramolecular ligand clustering induces integrin redistribution and cytoskeletal remodeling, leading to mitochondrial reorganization and a selective constraint on oxidative phosphorylation. This respiratory limitation functionally constrains tumor cell migration and invasion and cannot be overcome by restoring cytoskeletal contractility, whereas replenishing mitochondrial metabolic substrates effectively rescues motility. In a HeLa xenograft model, the integrin-binding supramolecular system suppresses tumor growth and reduces extracellular matrix deposition. These findings identify mitochondrial respiratory capacity as a critical downstream effector of integrin mechanosignaling and establish extracellular ligand organization as a previously unrecognized driver of mechanically encoded metabolic regulation.

Heterochromatin protein 1 (HP1 is an intrinsic component of heterochromatin domains where it is involved in a diverse set of functions including heterochromatin spreading and organization, chromatin compaction and transcriptional silencing. It has been suggested that HP1 functions through a phase separation mechanism, a process that has been observed in vitro in the presence of N-terminal phosphorylation, nucleic acids and nucleosome arrays. HP1 can also interact with numerous binding partners that contain a specific motif called an HP1 access code (HAC). HACs recognize and bind to an interface formed by the chromoshadow (CSD) domains in the HP1 homodimer, the functional form of the protein. It has been shown that some HP1 binding partners can enhance its phase separation ability while others disrupt the process. Here, we focus on the interactions between HP1 and three binding partners, namely the p150 subunit of the chromatin assembly factor 1 (CAF-1), the N-terminal domain of the lamin B receptor (LBR), and the mitotic protein Shugoshin 1 (Sgo1). Using phase separation assays, we show that CAF-1 prevents HP1 phase separation while LBR and Sgo1 enhance it. Binding assays, mutational studies, NMR spectroscopy and computational analysis allow us to dissect the contributions of the HAC motifs, the charge patterns of the binding partner sequences and the role of N-terminal phosphorylation on HP1 in condensate formation. Our results demonstrate that each binding partner uniquely balances these contributions to modulate the properties of HP1, while electrostatic interactions dominate the regulation of phosphorylated HP1. These results suggest that HP1 binding partners play an important role in the modulation of its properties and the regulation of its functions in distinct biological contexts.

Blockade by sedimentary particles, such as mineral crystals, is a continuous risk the kidney tubule faces. To prevent that, kidney resident macrophages form transepithelial protrusions and remove intratubular sedimentary particles, a behavior particularly prevailing in the medulla over the cortex. However, the molecular mechanisms underlying this characteristic behavior of medulla macrophages are incompletely understood. In this study, we identified that the medulla had higher mechanical stiffness than the cortex in steady state, which was further elevated when kidney stone formed. Increased tissue rigidity was sensed by medulla macrophages via mechanoreceptor Piezo1, which promoted macrophage protrusion formation and their ability to clean the tubules. Loss of Piezo1 expression in kidney macrophages predisposed mice to intratubular accumulation of mineral crystal in steady state and accelerated kidney stone formation during oxalate intake challenge. Signaling via Piezo1 mobilized molecules involved in cell adhesion and protrusion assembly, including Talin2 and focal adhesion kinase (FAK). Finally, we developed a first-of-its-kind cell-based therapy for the treatment of experimental nephrolithiasis by exploiting macrophage Piezo1 activity, and this strategy shows great promise for future translational research.

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.

The incidence of early-onset colorectal cancer (CRC) has risen sharply in recent decades1, yet the biological basis underlying the distinct behavior of tumors arising in young versus aged tissues remains poorly understood. Here we show that aging reprograms the epigenetic landscape of the colon, restricting colon tumor growth through stable silencing of developmental and fetal gene programs. We find that colon tumors arising in aged mice are intrinsically less proliferative than those arising in young animals. Multi-omic profiling of normal colon and colon tumors reveals that aging drives DNA hypermethylation, loss of Polycomb-associated chromatin states, and reduced chromatin accessibility at a defined set of developmental genes that are bivalent (marked by both H3K27me3 and H3K4 methylation), transcriptionally active in colon tumors from young animals and repressed in both tumors and normal tissue from old animals. Among the genes most strongly repressed in old animals is Tacstd2 (Trop2), a regulator of fetal intestinal programs and epithelial stemness. Pharmacologic inhibition of DNA methylation reactivates the aging-silenced gene network in organoids from old animals, whereas genetic disruption of Tacstd2 suppresses growth and developmental transcriptional programs in young tumor organoids. TACSTD2, fetal gene signatures, and the aging-associated bivalent gene program are likewise repressed in late-onset vs. early-onset human colorectal cancers. Collectively, these findings identify age-associated epigenetic silencing of developmental gene programs as a causal mechanism that constrains colorectal tumor growth and provide a mechanistic framework for understanding the distinct biology of early-onset colorectal cancer.

Joint music making offers an ecologically powerful framework for investigating human social interaction and synchronization. Yet, experimental paradigms often rely on traditional instruments that limit accessibility, reproducibility, and experimental control. In parallel, the use of music for therapy and rehabilitation is expanding, motivating the development of digital musical instruments that can serve research, educational, and clinical purposes. Here, we introduce the e-Music Box Roma (eMB Roma), an open, reproducible digital musical instrument designed to study music making behavior regardless of musical training. The eMB Roma plays preregistered music with tempo controlled by hand rotary movements. Building on the original e-Music Box (Novembre et al., 2015), the eMB Roma retains its intuitive rotary hand control while introducing major innovations: a fully open and 3D-printable design, modular hardware with integrated slider and button controls, polyphonic output with multiple simultaneous instruments, and MIDI compatibility. Additionally, a dedicated graphical user interface allows real-time monitoring, experiment control, device synchronization (like neuroimaging or motion capture devices), and both solo and joint music-making paradigms. The eMB Roma provides a flexible and accessible platform for research contexts, allowing experimental control, reproducibility, and future extensions. Its open design and modularity make it suitable not only for research but also for therapeutic, rehabilitation, and educational applications, where it can support personalized interventions and quantitative assessment of motor performance.

Extrahepatic delivery of lipid nanoparticles (LNPs) to non-phagocytic cells is a major challenge, with the leading strategy involving surface functionalization with target-specific monoclonal antibody (mAb) ligands. We investigate the stability of mAb-conjugated LNPs using two anchoring systems: the commonly used DSPE-PEG2kDa-maleimide and a block copolymer, PCL5kDa-b-PEG2kDa -maleimide, with the hypothesis that conjugation to a 150,000 Da antibody could overwhelm the relatively small ~600 Da aliphatic anchor on the PEG-lipid in vivo. Shedding of the mAB would compromise targeting. Conjugation integrity following IV injection was assessed by tagging LNPs and mAbs with metal ion tracers that could be quantified by ICP-MS. Results show that DSPE-PEG-mAb rapidly (within 1h) dissociates from LNPs in blood, leading to accelerated LNP clearance. In contrast, mAbs conjugated using PCL-b-PEG remained stably associated with the LNP over the 24h circulation and clearance of the construct. Results are connected to a thermodynamic model that reproduces experimental findings for PEG-anchor(-mAb) shedding in vitro and in vivo. This study identifies anchoring strength as a critical, unconsidered parameter for in vivo performance when conjugating mAbs to LNPs for extrahepatic delivery.

Representations of the same physical stimulus vary between individuals. Characterizing individual differences has practical implications, but is challenging because these representations are not directly observable. Given a model of how representations vary within a population, we propose a Bayesian adaptive procedure for estimating an individual observer's representation from a series of targeted perceptual discrimination judgments. A key component of our approach is using Fisher information to identify stimulus distortions that efficiently differentiate observers in the population. As a proof of concept, we focus on individual differences in color perception and simulate observers with cone fundamentals drawn from an individual colorimetric observer model. We demonstrate that our approach can recover key aspects of a sampled observer's cone fundamentals using simulated three-alternative forced-choice oddity judgments with approximately 500 trials, corresponding to an experimental duration of approximately one hour. Our Bayesian adaptive framework provides a promising and generalizable approach to efficiently link behavioral measurements to individual differences in sensory representations.