Single-cell Activity Research Articles

Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA.

Pathway analysis is a crucial analytical phase in disease research on single-cell RNA sequencing (scRNA-seq) data, offering biological interpretations based on prior knowledge. However, currently available tools for generating cell-level pathway activity scores (PAS) exhibit computational inefficacy in large-scale scRNA-seq datasets. Additionally, disease-related pathways are often identified through cross-condition comparisons within specific cell types, overlooking potential patterns that involve multiple cell types. Here, we present single-cell pathway activity factor analysis (scPAFA), a Python library designed for large-scale single-cell datasets allowing rapid PAS computation and uncovering biologically interpretable disease-related multicellular pathway modules, which are low-dimensional representations of disease-related PAS alterations in multiple cell types. Application on colorectal cancer (CRC) datasets and large-scale lupus atlas over 1.2 million cells demonstrated that scPAFA can achieve over 40-fold reductions in the runtime of PAS computation and further identified reliable and interpretable multicellular pathway modules that capture the heterogeneity of CRC and transcriptional abnormalities in lupus patients, respectively. Overall, scPAFA presents a valuable addition to existing research tools in disease research, with the potential to reveal complex disease mechanisms and support biomarker discovery at the pathway level.

Nonnegative matrix factorization for analyzing state dependent neuronal network dynamics in calcium recordings.

Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Nonnegative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.

Distinct inhibitory neurons differently shape neuronal codes for sound intensity in the auditory cortex.

Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons have distinct effects on population neuronal responses to noise bursts of varying intensities. We optogenetically stimulated SST or VIP neurons while simultaneously measuring the calcium responses of populations of hundreds of neurons in the auditory cortex of male and female awake, head-fixed mice to sounds. Upon SST neuronal activation, noise bursts representations became more discrete for different intensity levels, relying on cell identity rather than strength. By contrast, upon VIP neuronal activation, noise bursts of different intensity level activated overlapping neuronal populations, albeit at different response strengths. At the single-cell level, SST and VIP neuronal activation differentially modulated the response-level curves of monotonic and nonmonotonic neurons. SST neuronal activation effects were consistent with a shift of the neuronal population responses toward a more localist code with different cells responding to sounds of different intensity. By contrast, VIP neuronal activation shifted responses towards a more distributed code, in which sounds of different intensity level are encoded in the relative response of similar populations of cells. These results delineate how distinct inhibitory neurons in the auditory cortex dynamically control cortical population codes. Different inhibitory neuronal populations may be recruited under different behavioral demands, depending on whether categorical or invariant representations are advantageous for the task.Significance Statement Information about sounds is represented in the auditory cortex by neuronal population activity that has a characteristic sparse structure. Cortical neuronal populations comprise multiple types of excitatory and inhibitory neurons. Here, we find that activating different types of inhibitory neurons differentially controls population neuronal representations, with one type of inhibitory neurons increasing the differences in the identity of the cells recruited to represent the different sounds, and another inhibitory neuron type changing the relative activity level of overlapping neuronal populations. Such transformations may be beneficial for different types of auditory behaviors, suggesting that these different types of inhibitory neurons may be recruited under different behavioral constraints in optimizing neuronal representations of sounds.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Fluorescently probing anaerobic digester sludge: Measuring single-cell anabolic activity in methanogens (Methanosarcina and Methanothermobacter) with deuterium-labeled Raman analysis

This study addresses the challenge of obtaining in situ information on substrate utilization rates for individual microbial species in complex microbial communities such as anaerobic digester sludge. To overcome this hurdle, a novel approach combining doubly-labelled deuterium, fluorescence in situ hybridization (FISH) and Raman microspectroscopy was developed. The method enables quantitative determination of anabolic heavy hydrogen incorporation into FISH-targeted, exemplified by methanogenic cells from the genera Methanosarcina and Methanothermobacter.The deuterium incorporation rates ascertained by Raman red-shifting of C-Hx vibrational region to C-Dx vibrations, quantified through Raman peak area ratios, were compared for different carbon sources. Methanosarcina exhibited highest kinetic rates with acetate and propionate, while Methanothermobacter demonstrated faster incorporation under acetate and methanol supplementation.This groundbreaking study demonstrates the feasibility of obtaining quantitative metabolic rate information at a single-cell level using deuterium, FISH probes, and Raman microspectroscopy.

"Distinct inhibitory neurons differently shape neuronal codes for sound intensity in the auditory cortex".

Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons have distinct effects on population neuronal responses to noise bursts of varying intensities. We optogenetically stimulated SST or VIP neurons while simultaneously measuring the calcium responses of populations of hundreds of neurons in the auditory cortex of male and female awake, head-fixed mice to sounds. Upon SST neuronal activation, noise bursts representations became more discrete for different intensity levels, relying on cell identity rather than strength. By contrast, upon VIP neuronal activation, noise bursts of different intensity level activated overlapping neuronal populations, albeit at different response strengths. At the single-cell level, SST and VIP neuronal activation differentially modulated the response-level curves of monotonic and nonmonotonic neurons. SST neuronal activation effects were consistent with a shift of the neuronal population responses toward a more localist code with different cells responding to sounds of different intensity. By contrast, VIP neuronal activation shifted responses towards a more distributed code, in which sounds of different intensity level are encoded in the relative response of similar populations of cells. These results delineate how distinct inhibitory neurons in the auditory cortex dynamically control cortical population codes. Different inhibitory neuronal populations may be recruited under different behavioral demands, depending on whether categorical or invariant representations are advantageous for the task.

Tailoring a CRISPR/Cas-based Epigenome Editor for Programmable Chromatin Acylation and Decreased Cytotoxicity.

Engineering histone acylation states can inform mechanistic epigenetics and catalyze therapeutic epigenome editing opportunities. Here, we developed engineered lysine acyltransferases that enable the programmable deposition of acetylation and longer-chain acylations. We show that targeting an engineered lysine crotonyltransferase results in weak levels of endogenous enhancer activation yet retains potency when targeted to promoters. We further identify a single mutation within the catalytic core of human p300 that preserves enzymatic activity while substantially reducing cytotoxicity, enabling improved viral delivery. We leveraged these capabilities to perform single-cell CRISPR activation screening and map enhancers to the genes they regulate in situ. We also discover acylation-specific interactions and find that recruitment of p300, regardless of catalytic activity, to prime editing sites can improve editing efficiency. These new programmable epigenome editing tools and insights expand our ability to understand the mechanistic role of lysine acylation in epigenetic and cellular processes and perform functional genomic screens.

Visualizing PIEZO1 Localization and Activity in hiPSC-Derived Single Cells and Organoids with HaloTag Technology.

PIEZO1 is critical to numerous physiological processes, transducing diverse mechanical stimuli into electrical and chemical signals. Recent studies underscore the importance of visualizing endogenous PIEZO1 activity and localization to understand its functional roles. To enable physiologically and clinically relevant studies on human PIEZO1, we genetically engineered human induced pluripotent stem cells (hiPSCs) to express a HaloTag fused to endogenous PIEZO1. Combined with advanced imaging, our chemogenetic platform allows precise visualization of PIEZO1 localization dynamics in various cell types. Furthermore, the PIEZO1-HaloTag hiPSC technology facilitates the non-invasive monitoring of channel activity across diverse cell types using Ca 2+ -sensitive HaloTag ligands, achieving temporal resolution approaching that of patch clamp electrophysiology. Finally, we used lightsheet imaging of hiPSC-derived neural organoids to achieve molecular scale imaging of PIEZO1 in three-dimensional tissue organoids. Our advances offer a novel platform for studying PIEZO1 mechanotransduction in human cells and tissues, with potential for elucidating disease mechanisms and targeted therapeutic development.

Lamellipodia-Mediated Osteoblast Haptotaxis Guided by Fibronectin Ligand Concentrations on a Multiplex Chip.

Skull morphogenesis is a complex, dynamic process involving two different germ layers and progressing to the coordinated, directional growth of individual bones. The mechanisms underlying directional growth toward the apex are not completely understood. Here, a microfluidic chip-based approach is utilized to test whether calvarial osteoblast progenitors undergo haptotaxis on a gradient of Fibronectin1 (FN1) via lamellipodia. Mimicking the embryonic cranial mesenchyme's FN1 pattern, FN1 gradients is established in the chip using computer modeling and fluorescent labeling. Primary mouse calvarial osteoblast progenitors are plated in the chip along an array of segmented gradients of adsorbed FN1. The study performs single-cell tracking and measures protrusive activity. Haptotaxis is observed at an intermediate FN1 concentration, with an average directional migration index (yFMI) of 0.07, showing a significant increase compared to the control average yFMI of -0.01. A significant increase in protrusive activity is observed during haptotaxis. Haptotaxis is an Arp2/3-dependent, lamellipodia-mediated process. Calvarial osteoblast progenitors treated with the Arp2/3 (Actin Related Protein 2/3 complex) inhibitor CK666 show significantly diminished haptotaxis, with an average yFMI of 0.01. Together, these results demonstrate haptotaxis on an FN1 gradient as a new mechanism in the apical expansion of calvarial osteoblast progenitors during development and shed light on the etiology of calvarial defects.

Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer

While heterogeneity is a key feature of cancer, understanding metabolic heterogeneity at the single-cell level remains a challenge. Here we present 13C-SpaceM, a method for spatial single-cell isotope tracing that extends the previously published SpaceM method with detection of 13C6-glucose-derived carbons in esterified fatty acids. We validated 13C-SpaceM on spatially heterogeneous models using liver cancer cells subjected to either normoxia-hypoxia or ATP citrate lyase depletion. This revealed substantial single-cell heterogeneity in labelling of the lipogenic acetyl-CoA pool and in relative fatty acid uptake versus synthesis hidden in bulk analyses. Analysing tumour-bearing brain tissue from mice fed a 13C6-glucose-containing diet, we found higher glucose-dependent synthesis of saturated fatty acids and increased elongation of essential fatty acids in tumours compared with healthy brains. Furthermore, our analysis uncovered spatial heterogeneity in lipogenic acetyl-CoA pool labelling in tumours. Our method enhances spatial probing of metabolic activities in single cells and tissues, providing insights into fatty acid metabolism in homoeostasis and disease.

Hepatic stellate cell single cell atlas reveals a highly similar activation process across liver disease aetiologies

Background & AimsThe progression of chronic liver disease (CLD) is characterized by excessive extracellular matrix deposition, disrupting hepatic architecture and function. Upon liver injury, hepatic stellate cells (HSCs) differentiate towards myofibroblasts and become inflammatory, proliferative and fibrogenic. To date it is still unclear whether HSC activation is driven by similar mechanisms in different aetiologies. MethodsHSCs from multiple publicly available single-cell RNA-sequencing datasets were annotated and merged into a single-cell HSC activation atlas. Spheroid co-cultures of primary mouse hepatocytes/HSCs (n=5) and ELISAs on patient plasma samples (n=80) were performed to validate the mechanistic insight obtained from the HSC atlas. ResultsWe established an HSC activation atlas in which HSCs are clearly divided into 3 distinct transcriptomic profiles: quiescent HSCs, initiatory HSCs and myofibroblasts. These transcriptomic profiles are present in each of the investigated mouse liver injury models as well as in human chronic liver diseases, indicating that HSC activation is a conserved process. This activation process is driven by a core set of transcription factors independent of liver injury or species. Furthermore, we reveal novel ligands associated with activation of HSCs in multiple liver injury models and validate the profibrotic effect of parathyroid hormone. Finally, we identify COLEC10 as a conserved marker for quiescent HSCs and a biomarker of liver fibrosis in patients with different CLDs (p<0.0001). ConclusionsWe reveal unexpected similarities in the regulatory mechanisms of HSCs across diverse liver injury settings and species. The HSC activation atlas has the potential to provide novel insights into liver fibrosis and steer novel treatment options. Impact and implicationsThis study establishes a single-cell atlas of hepatic stellate cells across various liver injuries, highlighting a conserved activation process between different injuries and across species. The discovery of novel activating ligands and the biomarker COLEC10 in human plasma enhances diagnostic and therapeutic strategies. Additionally, the conserved activation process supports the use of any mouse model for mechanistic studies and testing of new anti-fibrotic compounds, streamlining preclinical research efforts.

Perpetual step-like restructuring of hippocampal circuit dynamics

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies.

Neuronal and Behavioral Responses to Naturalistic Texture Images in Macaque Monkeys.

The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and therefore unlikely to directly reflect the formation of perceptual decisions.

ISECRETE: Integrating Microfluidics and DNA Proximity Amplification for Synchronous Single-Cell Activation and IFN-γ Secretion Profiling.

Cytokines, crucial in immune modulation, impact disease progression when their secretion is dysregulated. Existing methods for profiling cytokine secretion suffer from time-consuming and labor-intensive processes and often fail to capture the dynamic nature of immune responses. Here, iSECRETE, an integrated platform that enables synchronous cell activation, wash-free, and target-responsive protein detection for single-cell IFN-γ cytokine secretion analysis within 30min at room temperature is presented. By incorporating a DNA proximity assay (DPA) into a multifunctional microfluidic system, one-pot homogenous cytokine signal amplification, with a limit of detection of ≈50 secreted molecules per cell is achieved. iSECRETE can robustly handle various sample types that are shown. Two distinct immune activation assay modalities are demonstrated on iSECRETE. Finally, the detection of single-cell IFN-γ secretion as an activation hallmark of chimeric antigen receptor T cells within 6h of exposure to cancer targets is shown. iSECRETE represents the fastest single-cell sample-to-result cytokine secretion assay to date, providing a powerful tool for advancing the understanding of biological phenotypes, functions, and pathways under in vivo-like conditions.

FluoAnalysis: An Open-Source MATLAB Toolbox for Analysis of Calcium Imaging Measurements of Oscillatory Astrocytic and Neuronal Networks.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Ultrafine PtFeMo intermetallic compound nanowires for efficient oxygen reduction reaction in proton exchange membrane fuel cells

To advance the application of proton exchange membrane fuel cells (PEMFCs), it is crucial to develop high-performance oxygen reduction reaction (ORR) catalyst. Pt-based alloy nanowires exhibit excellent ORR activity due to their unique one-dimensional structure, but transition metals undergo spontaneous leaching under the harsh operating condition, often leading to decreased activity. Herein, ultrafine PtFeMo intermetallic compound nanowires were successfully synthesized as a high-performance ORR catalyst for PEMFC. The average diameter of the nanowires was 2.1 nm and the incorporation of inexpensive and high melting point metal Mo improved both catalytic performance and structural stability. The as-prepared ultrafine PtFeMo intermetallic compound nanowires exhibited mass activity of 2.56 A mgPt−1 for ORR, which is higher than that of disordered PtFeMo alloy nanowires (1.36 A mgPt−1) and commercial Pt/C (0.24 A mgPt−1) in rotating disk electrode test. And it exhibited peak power density of 2716 mW cm−2 under 250 kPa H2-O2 single-cell and mass activity of 0.62 A mgPt−1 under 100 kPa H2-O2 single-cell test in PEMFC. After long-time durability test, the intermetallic compound nanowires maintained their original structure, composition and electrocatalytic performance well, highlighting the importance of Mo incorporation and ordered phase structure generation.

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data

Single-cell analysis across multiple samples and conditions requires quantitative modeling of the interplay between the continuum of cell states and the technical and biological sources of sample-to-sample variability. We introduce GEDI, a generative model that identifies latent space variations in multi-sample, multi-condition single-cell datasets and attributes them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states, and machine learning-based prediction of sample characteristics from single-cell data. GEDI can also incorporate gene-level prior knowledge to infer pathway and regulatory network activities in single cells. Finally, GEDI extends all these concepts to previously unexplored modalities that require joint consideration of dual measurements, such as the joint analysis of exon inclusion/exclusion reads to model alternative cassette exon splicing, or spliced/unspliced reads to model the mRNA stability landscapes of single cells.

Accurate estimation of pathway activity in single cells for clustering and differential analysis.

Inferring which and how biological pathways and gene sets change is a key question in many studies that utilize single-cell RNA sequencing. Typically, these questions are addressed by quantifying the enrichment of known gene sets in lists of genes derived from global analysis. Here we offer SiPSiC, a new method to infer pathway activity in every single cell. This allows more sensitive differential analysis and utilization of pathway scores to cluster cells and compute UMAP or other similar projections. We apply our method to COVID-19, lung adenocarcinoma and glioma data sets, and demonstrate its utility. SiPSiC analysis results are consistent with findings reported in previous studies in many cases, but SiPSiC also reveals the differential activity of novel pathways, enabling us to suggest new mechanisms underlying the pathophysiology of these diseases and demonstrating SiPSiC's high accuracy and sensitivity in detecting biological function and traits. In addition, we demonstrate how it can be used to better classify cells based on activity of biological pathways instead of single genes and its ability to overcome patient-specific artifacts.

Serotonergic input from the dorsal raphe nucleus shapes learning-associated odor responses in the olfactory bulb.

Neural activity in the olfactory bulb (OB) can represent odor information during different brain and behavioral states. For example, the odor responses of mitral/tufted (M/T) cells in the OB change during learning of odor-discrimination tasks and, at the network level, beta power increases and the high gamma (HG) power decreases during odor presentation in such tasks. However, the neural mechanisms underlying these observations remain poorly understood. Here, we investigate whether serotonergic modulation from the dorsal raphe nucleus (DRN) to the OB is involved in shaping activity during the learning process in a go/no-go task in mice. Fiber photometry was used to record the population activity of DRN serotonergic neurons during a go/no-go task. Invivo electrophysiology was used to record neural activity (single units and local field potentials) in the OB during the go/no-go task. Real-time place preference (RTPP) and intracranial light administration in a specific subarea (iClass) tests were used to assess the ability of mice to encoding reward information. Odor-evoked population activity in serotonergic neurons in the DRN was shaped during the learning process in a go/no-go task. In the OB, neural activity from oscillations to single cells showed complex, learning-associated changes and ability to encode information during an odor discrimination task. However, these properties were not observed after ablation of DRN serotonergic neurons. The activity of neural networks and single cells in the OB, and their ability to encode information about odor value, are shaped by serotonergic projections from the DRN.

Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E.coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal's ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E.coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.