Revealing Single-Cell Heterogeneity of Labile Cu(I) Accumulation and Metabolism in Microalgae by Image-Enabled Flow Cytometry.

Cancer cells heterogeneity presents a major roadblock in clinical diagnostics and therapy. Investigations of tumor cell heterogeneity using single-cell analytical techniques have revealed not only the presence of multiple clonal subpopulations but also phenotypic variability among genetically identical cells. This heterogeneity plays a critical role in therapeutics, as population extrema, rather than cells near the ensemble mean, can dominate pathogenesis as well as drug resistance. To characterize single cell heterogeneity, multiple techniques quantifying single-cell gene expression have been developed. However, there's a lack of experimental techniques to measure how cellular decision-making processes underlying population variability derive from extracellular biochemical input signals, such as peptide growth factors, which cannot be measured at the single-cell level. Here, to digitally count growth factors in single cells, we develop a novel method combining fluorescent quantum dots and calibrated three-dimensional deconvolution microscopy (QDC-3DM). Using quantum dots with near-infrared emission to overcome intrinsic cellular autofluorescence, we were able to detect and accurately count individual quantum dots conjugated to epidermal growth factor (EGF) using their fluorescent intensities. Analyzing triple-negative breast cancer cells (MDA-MB-231) with QDC-3DM, we observed that single-cell heterogeneity in growth factor stimulation led to heterogeneity in receptor activation. When treating cells with increasing concentration of phamarcological inhibitor blocking receptor activation, we observed a proportional increase in receptor activation heterogeneity. Together, our results indicate that external stimulation contributes to signaling variation and drug response variation at the single-cell level. We anticipate that QDC-3DM can be applied to any peptidic ligands to study how extracellular signaling stimulation contributes to phenotypic variability to provide new insight into cancer cell heterogeneity that plays a critical role in therapeutic resistance. Citation Format: Phuong Le, Brian C. Baculis, Hee Jung Chung, Kristpher Kilian, Andrew M. Smith. Investigating how extracellular signals contribute to single cancer cell heterogeneity using near-infrared quantum dots [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-330.

Background: Tumor-associated macrophages (TAMs) play key roles in the progression of cancers and their response to various treatments, for example through secretion of pro-survival growth factors; contribution to angiogenesis; and modulation of the tumor immune response via antigen presentation and expression of factors such as PD-L1. TAM effects can be highly localized within the tumor microenvironment, for example through cell-cell contact, yet it has been difficult to directly study interactions between TAMs and tumor cells at high resolution, in orthotopic sites of cancer development, and in real-time as cells respond to therapeutic treatment. To understand the biology underlying heterogeneous interactions between TAMs and tumor cells, we used in vivo fluorescence imaging at single-cell resolution to study pharmacodynamic (PD) response of tumor cells and TAMs to chemotherapeutic treatment. Methods: To directly image host leukocytes in a variety of tumor models, we used a combination of fluorescent genetic reporter host animals (CX3CR1GFP/+ reporter mice) and macrophage labeling via well-validated fluorescent and magnetic dextran-coated nanoparticles. For direct visualization of therapeutic response in cancer cells themselves, we used 53BP1-mApple as a transgenic fluorescent reporter to quantify DNA damage responses at the single-cell level. Automated computational segmentation of 3D images enabled precise quantification of statistical correlation between spatial TAM localization and cellular DNA damage responses. We performed studies in multiple human xenograft and syngeneic tumor models, including orthotopic ovarian cancer models and a syngeneic Kras-mutant p53-null lung adenocarcinoma model; histology validated in vivo imaging results. As a proof-of-principle, we tested cellular response to platinum (Pt)-based DNA-damaging agents. Results: Using a novel combination of orthotopic imaging setups, fluorescent reporters for TAMs and cancer cell pharmacodynamic response, along with automated 3D image segmentation and single-cell quantification, here we demonstrate that in vivo imaging can quantify correlations between local TAM concentrations and the simultaneously observed behavior of thousands of cancer cells. As a proof-of-principle, we measure relationships between single-cell DNA damage and local TAM levels in response to Pt-based chemotherapeutic treatment, and find that highly-localized concentrations of tumor-associated immune cells can influence therapeutic response at the single-cell level (and at cellular length-scale gradients). Importantly, we find these relationships to be highly probabilistic (rather than deterministic), and automated classification of thousands of cells across multiple tumors enables robust quantification of the inherent stochasticity involved in the pharmacodynamic responses. We find that single-cell heterogeneity and its explanation by microenvironmental factors such as local TAM concentration and vascular proximity can be heavily dependent on the therapeutic treatment and tumor model. Conclusions: This work presents in vivo imaging technology that should be useful for studying interactions between cancer cells and TAMs in orthotopic sites including intraperitoneal ovarian cancer and can be extended to other cell interactions (for instance by using fluorescent genetic reporters of various lymphocyte populations). Interaction and co-localization between tumor cells and associated immune cells can influence therapeutic response at the single-cell level, and such information will likely be useful for understanding heterogeneous drug responses to chemotherapeutics and immunotherapeutics alike. Citation Format: Miles A. Miller, Mikael Pittet, Ralph Weissleder. In vivo imaging of innate immune cells to measure drug response. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr B133.

Metastatic breast cancer is the most aggressive form of breast cancer leading to poor disease-free patient survival and accounting for 90% of all breast cancer-related deaths. The specific mechanisms that promote metastasis are not fully understood owing to the complex interplay between cancer cells and their surrounding microenvironment. This microenvironment involves clonal and sub-clonal heterogeneity in genomic, transcriptomic and protein signaling network across different cancer cell sub-populations. Understanding the molecular mechanism behind cancer metastasis demands state of the art single cell and spatial technologies that can provide deeper insight on disease progression. Since, cancer is heavily influenced by genetic changes, the study of the 3D- genome organization in single cells helps to improve understanding of disease progression and prognosis. The genome is organized and packaged in a highly structured manner inside the nucleus of mammalian cells and exhibits dynamic reorganization throughout disease progression. Understanding this 3D organization at the single-cell level with high spatial resolution is crucial for disease research. In this abstract, we present a novel jebFISHTM protocol on the PaintScapeTM platform that can be used to understand differences in global 3D genome organization among several localized and metastatic breast cancer cell lines at single cell, sub-population and population level. Using a highly multiplexed genome wide panel of chromosomal targets, we will show disruption in chromosome territory, radius of gyration, chromosomal instability between localized and metastatic breast cancer cells at single cell and sub-population level. We will also show the signature of specific 3D genome structural alterations and interchromosomal interactions related to breast cancer metastasis compared to normal breast cells. In addition, we investigated differences in global 3D genome architecture of different breast cancer sub-types such as HER2 enriched vs triple negative breast cancer cells. We will show how different breast cancer sub-types carry specific chromosomal instability and differential interchromosomal interaction patterns at single cell, sub-chromosomal and sub-population level. We envision this study will improve our understanding on breast cancer disease progression and provide deeper insight into the underlying genomic heterogeneity of single cancer cells and sub-populations which might help to design better treatment options in the future. We propose that this study serves as proof-of-principle and is generalizable to other types of cancers where changes in 3D genome organization may occur at different cancer stages. Citation Format: Shyamtanu Chattoraj, Huy Nguyen, Jude Dunne. Mapping single cell spatial 3D genome landscape of localized and metastatic breast cancer sub-types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2852.

One of the leading causes of tumor cell heterogeneity is the complex nature of the tumor immune microenvironment involving crosstalk between different signaling pathways of cancer cells and immune cells. For cancer cells to grow into a primary tumor and then metastasize into secondary tumors, they must avoid or overcome the attack of different types of immune cells, including macrophages, natural killer (NK) cells, and T lymphocytes. For example, cancer cells can polarize macrophages from “tumor killing” to “tumor promoting” type using specific signaling molecules. Such tumor immune crosstalk is dependent on the clonal and sub-clonal heterogeneity in genomic, transcriptomic and protein signaling networks across different cancer cell sub-populations, immune cells and its surrounding microenvironment. Since structural aberrations affect gene dysregulation, the study of the 3D- genome organization in single cells can help in better understanding of disease progression and prognosis. The genome is organized and packaged in a highly structured manner inside the nucleus of mammalian cells and exhibits dynamic reorganization throughout disease progression. Understanding this 3D organization at the single-cell level with high spatial resolution is crucial for immune-oncology disease research such as investigating tumor-immune cell interactions. In this abstract, we present a novel jebFISHTM protocol on the PaintScapeTM platform that can be used to investigate immune cell modulated differences in global 3D genome organization among different localized and metastatic breast cancer cell lines at single cell, sub-population and population level. Using a genome wide panel of chromosomal targets on an onco-immune co-culture system involving different breast cancer cell lines and CD4+ T-cells or macrophages, we will show disruption in chromosome territory, radius of gyration, chromosomal instability between localized and metastatic breast cancer cells at single cell and sub-population level. We observed signatures of specific 3D genome structural alterations and interchromosomal interactions in cancer cells and/or immune cells, related to the effect of different types of immune response. We will show how the 3D architecture of key genomic loci and its associated regulatory elements belonging to major cancer signaling pathways exhibit specific signatures including differential folding, chromosomal instability, differential interchromosomal interaction and structural variation patterns at single cell, sub-chromosomal and sub-population level. We envision this study will improve our understanding on breast cancer tumor immune microenvironment and provide deeper insight into the underlying genomic heterogeneity of single cancer cells and sub-populations which might help to design better treatment options in the future. Citation Format: Jude Dunne, Huy Nguyen, Shyamtanu Chattoraj. Mapping spatial 3D genome landscape of breast cancer cells in different tumor immune microenvironments using onco-immune co-culture as a model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5241.

Across species and environments, the ribosome content of cell populations correlates with population growth rate. The robustness and universality of this correlation have led to its classification as a "growth law." This law has fueled theories about how evolution selects for microbial organisms that maximize their growth rate based on nutrient availability, and it has informed models about how individual cells regulate their growth rates and ribosomal content. However, due to methodological limitations, this growth law has rarely been studied at the level of individual cells. While populations of fast-growing cells tend to have more ribosomes than populations of slow-growing cells, it is unclear whether individual cells tightly regulate their ribosome content to match their environment. Here, we employ recent groundbreaking single-cell RNA sequencing techniques to study this growth law at the single-cell level in two different microbes, S. cerevisiae (a single-celled yeast and eukaryote) and B. subtilis (a bacterium and prokaryote). In both species, we observe significant variation in the ribosomal content of single cells that is not predictive of growth rate. Fast-growing populations include cells exhibiting transcriptional signatures of slow growth and stress, as do cells with the highest ribosome content we survey. Broadening our focus to non-ribosomal transcripts reveals subpopulations of cells in unique transcriptional states suggestive that they have evolved to do things other than maximize their rate of growth. Overall, these results indicate that single-cell ribosome levels are not finely tuned to match population growth rates or nutrient availability and cannot be predicted by a Gaussian process model that assumes measurements are sampled from a normal distribution centered on the population average. This work encourages the expansion of growth law and other models that predict how growth rates are regulated or how they evolve to consider single-cell heterogeneity. To this end, we provide extensive data and analysis of ribosomal and transcriptomic variation across thousands of single cells from multiple conditions, replicates, and species.

Background: Dysregulated cellular metabolism is a hallmark of breast cancer, and targeting it has promising implications for improving care and patient outcomes. Specifically, heterogeneity in tumor metabolism is thought to play a role in determining chemotherapy response, the development of resistance, and promoting metastastasis. Despite this, metabolic tumor heterogeneity for individual breast cancer patients has not been characterized completely. Methods: In this study, we used state-of-the-art techniques to characterize metabolic heterogeneity within individual patient tumors by integrating single cell RNA-seq data with genome-scale metabolic modeling. Using SimBioSys’ TumorScope - a commercially available biophysical modeling platform, we compared intra-tumoral metabolic heterogeneity from experimental single cell RNA-seq data to simulated intra tumoral heterogeneity. Results: Using single cell RNA-seq data, we found that intra-tumoral gradients in nutrient availability are widely present within patient tumors (for a single luminal A patient, glucose import flux ranged from 0.19 - 1.25 g/gDW/day, while glutathione import ranged from 0.004 - 0.054 g/gDW/day). We also found that these gradients lead to cellular growth rate gradients within individual tumors (for our representative patient, median SGR = 0.62 %/day +/- 0.33 %/day stdev). Using TumorScope, we found this same gradient behavior within patient tumors. Selecting a similarly growing luminal A patient from our TumorScope simulations resulted in gradients in glucose import (range = 0.17 - 1.26 g/gDW/day), glutathione import (range = 0.024 - 0.058 g/gDW/day), and tumor SGR (median = 0.40 %/day, stdev = 0.42 %/day), which closely match metabolism from single cells (comparing maximum-scaled SGR distributions between single cells and TumorScope yielded a p-value = 0.10). We next examined which nutrients govern heterogeneity in tumor SGR. We found that glucose availability with the tumor microenvironment is more limiting to cell growth than oxygen availability, and this result was consistent between metabolic profiles from both single cell RNA-seq data and TumorScope simulations. TumorScope’s spatially resolved simulations offered the additional insight that gradients in nutrient availability are caused by heterogeneity in the distribution of macro- and micro-vasculature and the composition of the tumor microenvironment. We then used data reduction techniques to compare populations of single cells with differing metabolic phenotypes to identify molecular behavior at the single cells in higher molecular resolution. We found that single cells collected from the clinic co-cluster with single cells from TumorScope simulations, suggesting that a significant amount of intra-tumoral metabolic heterogeneity observed in patients is captured by TumorScope simulations. Conclusion: Accessing tumor heterogeneity has traditionally required specialized equipment, analytic expertise, and invasive procedures, largely limiting its study to large, academic hospitals. Currently, metabolic heterogeneity is only understood in 2D and for few markers (using pathology slides) or in relatively few cells with little or no spatial resolution (for single cell RNA-seq). TumorScope provides a novel approach to simulate metabolic heterogeneity at the single cell scale in 3D across a whole tumor. TumorScope democratizes the study of tumor heterogeneity by making it accessible to clinicians and researchers from MRI data alone. TumorScope has the capability to capture tumor metabolic heterogeneity at a higher scale than previously achievable which will allow for a dramatic increase in our understanding of tumor biology and ultimately improve clinical decision making. Citation Format: Daniel J Cook, John Whitman, Nicole Liadis, John Cole. Spatially-resolved single-cell tumor heterogeneity captured by TumorScope biophysical modeling software using MR Imaging [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-05-07.

Understanding the interactions between mercury and microalgae, especially the interactions between inorganic mercury (IHg) and extracellular polymeric substances (EPS, a protective barrier between cells and their external environment), is essential for elucidating mercury's toxicological mechanisms. Given the inherent cell heterogeneity, a novel analysis system of an online viscoelastic fluid focusing chip-time-resolved analysis inductively coupled plasma mass spectrometry has been developed to investigate the bioaccumulation of HgS nanoparticles and Hg2+ in single Microcystis aeruginosa (M. aeruginosa) cells, exploring the interaction mechanisms between HgS/Hg2+ accumulation in algal cells and EPS. The single-cell analysis results reveal minimal bioavailability of HgS within algal cells, with mercury's toxicity to M. aeruginosa being species-dependent. Notably, algal cells exhibited more heterogeneity in HgS uptake than in Hg2+ uptake. Under Hg2+/HgS stress, M. aeruginosa cells with EPS removed (EPS-R algal cells) showed an increased level of bioaccumulation of mercury compared to those with EPS (EPS-C algal cells), highlighting the critical role of EPS in mercury bioaccumulation. Overall, the designed viscoelastic fluid microfluidic focusing chip integrates focusing and cleaning functions, featuring easy fabrication, simple operation, low sample loss, and relatively high throughput. Under the optimal conditions, the sample throughput is 1195 min-1 and the cell recovery is 90%. Besides, this research offers novel insights into the interaction mechanisms between Hg2+/HgS and EPS in microalgal cells and unveils the specific toxic effects of Hg2+/HgS on M. aeruginosa at the single-cell level, contributing to a deeper understanding of mercury's ecological and toxicological impact in aquatic environments.

Transient phases of OXPHOS inhibitor resistance reveal underlying metabolic heterogeneity in single cells

Evaluation of Cd2+ stress on Synechocystis sp. PCC6803 based on single-cell elemental accumulation and algal toxicological response

Detection of single cell heterogeneity in cancer

Apoptotic process is highly heterogeneous, and a long-standing question is how many parameters define time and reversibility of the apoptotic response at a population and single-cell levels. Cell death analysis applications have greatly expanded since the introduction of flow cytometry. Classical approach for evaluation of apoptosis is en masse analysis of cells treated with different stimuli, but these methods cannot demonstrate heterogeneity in the population. Single-cell heterogeneity is now usually assessed by multicolor fluorescence microscopy; however obtaining reasonable statistics is time consuming and laborious. Therefore we combined flow cytometry, imaging flow cytometry, and fluorescent microscopy to characterize at a single-cell and population level sequence of apoptotic events induced by a variety of treatments (Vorobjev, Barteneva, J Histochem Cytochem 63:494-510, 2015). We show that simultaneous use of membrane potential dye TMRE, caspases 3/7 sensor, Annexin V and nuclear staining along with morphological parameters demonstrate heterogeneity of the whole process and is a valuable method for quantitative study of the apoptosis execution. Imaging flow cytometry allowed us to analyze correlation between TMRE, caspases 3/7, and Annexin V staining and morphological characteristics providing valuable information on the process of apoptotic execution. Importantly, comparisons of different data sets obtained by three methods allowed us to achieve temporal resolution of the whole process superior to that had been obtained by only one method.

The uptake and accumulation of metals occurs in the kidney, which is a key site for interaction between metal nephrotoxicants. The uptake/accumulation and interaction of CdCl2, HgCl2, K2Cr2O7, and NaAsO2 was examined in precision-cut rabbit renal cortical slices. Slices were incubated with 10(-6) to 10(-3) M of a single metal toxicant or combinations of metal toxicants for 12 hr in DME-F12 media. Slices were blotted and sandwiched between two mylar films stretched across XRF sample cups. Quantitation of the metal in the slices was performed by proton-induced X-ray emission analysis (PIXE). The uptake of the metals was rapid, often reaching a maximum between 3 to 6 hr; the accumulation of Hg was highest, followed in order by Cd, Cr, and As. When two metals were present together, substantial alterations were observed in the uptake of the metals in the slices. HgCl2 hindered the uptake of K2Cr2O7, NaAsO2, CdCl2 (in this order), whereas these metals facilitated the uptake of HgCl2. However, a decreased uptake of both metals was often noted after exposure to other combinations of metals. PIXE analysis of metal content in slices is attractive since all elements (atomic number > 20) can be determined simultaneously. This information will be particularly useful in studying potential toxic interactions.

It is now well appreciated that tumor growth depends on the tumor microenvironment (TME) comprising various non-malignant stromal cell types, including cancer-associated fibroblasts (CAFs). Cancer-associated fibroblasts (CAFs) are responsible for secreting the extracellular matrix (ECM) prominent in certain solid tumors such as pancreatic adenocarcinoma (PDAC). Despite our increasing knowledge of CAFs, therapeutic targeting of CAFs is currently not a feasible strategy, as CAFs are highly heterogeneous and extremely plastic, with each CAF population able to adopt both tumor-promoting and -restraining functions. We hypothesize that the plasticity and functional heterogeneity of CAFs can be explained by their flexible metabolism in response to environmental cues and the existence of discrete metabolic states within CAF subpopulations. While metabolic states can be predicted from single-cell transcriptomic data, this does not provide information on metabolic activity and potential, nor does it allow perturbations required for validation. Scalable and tractable methods to assess metabolic activity and CAF heterogeneity in parallel at the single cell level are needed to distinguish numerous existing CAF states do not exist. To address this need, in this work, we developed a strategy to study CAF metabolic activity and phenotypic heterogeneity at the single-cell level. We designed and optimized a 25-plex immunophenotyping panel of CAF and fibroblast markers that can be combined with numerous metabolic probes via Full Spectrum Profiling (FSP) flow cytometry. In addition, we compiled an analysis pipeline to facilitate the analysis of high-dimensional flow cytometry data. Using established 2D and 3D mono- and co-culture assays, we demonstrate the potential of our panel to be multiplexed with chemical probes for cell proliferation, lipid content, lipid uptake, fatty acid oxidation, glucose uptake, cholesterol content, hypoxia, or mitochondrial potential. Deploying our strategy in a KPC organoid-derived murine model of PDAC, we observed subpopulations of CAFs with differing metabolic potentials that are consistent with our predictions from transcriptomic metabolic modeling. With the high throughput and single-cell nature of FSP, our strategy represents a novel and modular tool for studying CAF phenotypic and metabolic heterogeneity. Citation Format: Kevin Muñoz Forti, Marta Storl-Desmond, Daniel Martínez, Sayana Issac, Shan Cao, Simon Schwörer. Full spectrum profiling flow cytometry to study cancer-associated fibroblast heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2667.

Introduction: Cancers are heterogeneous and often genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type occurring within different individuals. However, it does not yet address heterogeneity at the single cell level within individual cancers or the dynamic nature of cancer, due to heritable genetic and epigenetic change, as well as transient functional changes. We established methods for evaluating personalized medicine strategies, and compared the current personalized medicine strategy to alternatives. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression. This strategy focuses on the average, static, and current properties of the sample. Next-generation strategies also consider minor sub-clones, dynamics, and predicted future tumor states. Methods: We developed a mathematical model of targeted cancer therapy incorporating genetic evolutionary dynamics and single cell heterogeneity, and examined simulated clinical outcomes (cell numbers of clones and sub-clones, projected survival). We compared the current personalized medicine strategy to 5 alternative personalized strategies. The latter strategies explicitly considered sub-clones, evolutionary dynamics, and likely future sub-clones in addition to the current predominant clone. Particular emphasis was given to the prevention of incurable, multiply resistant sub-clones. Results: We carried out a computerized virtual clinical trial of over 3 million evaluable cancer “patients,” comparing current personalized medicine and 5 alternative strategies. While the current personalized medicine strategy was equally effective to the alternatives in 2/3 of the cases, in 1/3 of the cases alternative strategies led to improved outcomes. All alternatives tested resulted in an approximate doubling in mean and median survival compared to current personalized medicine and an increase in the apparent cure rate from 0.7% for current personalized medicine to 17-20% for alternatives. In no case was the current personalized medicine strategy superior. Conclusions: These findings may lead to improved patient outcomes. Further, they suggest global enhancements to translational oncology research paradigms: for example, molecular characterization of incurable, multiply resistant “end states” from autopsy may be equally or more important than characterizing initial diagnostic states. We have developed methods to evaluate alternative personalized medicine strategies. Next generation strategies may consider sub-clones, evolutionary dynamics, and predicted future states. Application of knowledge from growing molecular and empirical oncology databases may allow more informative therapeutic simulations than previously possible. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-448. doi:1538-7445.AM2012-LB-448

Viral infection outcomes are governed by the complex and dynamic interplay between the infecting virus population and the host response. It is increasingly clear that both viral and host cell populations are highly heterogeneous, but little is known about how this heterogeneity influences infection dynamics or viral pathogenicity. To dissect the interactions between influenza A virus (IAV) and host cell heterogeneity, we examined the combined host and viral transcriptomes of thousands of individual cells, each infected with a single IAV virion. We observed complex patterns of viral gene expression and the existence of multiple distinct host transcriptional responses to infection at the single cell level. We show that human H1N1 and H3N2 strains differ significantly in patterns of both viral and host anti-viral gene transcriptional heterogeneity at the single cell level. Our analyses also reveal that semi-infectious particles that fail to express the viral NS can play a dominant role in triggering the innate anti-viral response to infection. Altogether, these data reveal how patterns of viral population heterogeneity can serve as a major determinant of antiviral gene activation.