Publications by authors named "James M McFarland"

27 Publications

  • Page 1 of 1

Bridging the gap between cancer cell line models and tumours using gene expression data.

Br J Cancer 2021 Mar 29. Epub 2021 Mar 29.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Cancer cell line models are a cornerstone of cancer research, yet our understanding of how well they represent the molecular features of patient tumours remains limited. Our recent work provides a computational approach to systematically compare large gene expression datasets to better understand which cell lines most closely resemble each tumour type, as well as identify potential gaps in our current cancer models.
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http://dx.doi.org/10.1038/s41416-021-01359-0DOI Listing
March 2021

A first-generation pediatric cancer dependency map.

Nat Genet 2021 04 22;53(4):529-538. Epub 2021 Mar 22.

Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.
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http://dx.doi.org/10.1038/s41588-021-00819-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049517PMC
April 2021

Integrated cross-study datasets of genetic dependencies in cancer.

Nat Commun 2021 03 12;12(1):1661. Epub 2021 Mar 12.

Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.
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http://dx.doi.org/10.1038/s41467-021-21898-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955067PMC
March 2021

Aneuploidy renders cancer cells vulnerable to mitotic checkpoint inhibition.

Nature 2021 02 27;590(7846):486-491. Epub 2021 Jan 27.

Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Selective targeting of aneuploid cells is an attractive strategy for cancer treatment. However, it is unclear whether aneuploidy generates any clinically relevant vulnerabilities in cancer cells. Here we mapped the aneuploidy landscapes of about 1,000 human cancer cell lines, and analysed genetic and chemical perturbation screens to identify cellular vulnerabilities associated with aneuploidy. We found that aneuploid cancer cells show increased sensitivity to genetic perturbation of core components of the spindle assembly checkpoint (SAC), which ensures the proper segregation of chromosomes during mitosis. Unexpectedly, we also found that aneuploid cancer cells were less sensitive than diploid cells to short-term exposure to multiple SAC inhibitors. Indeed, aneuploid cancer cells became increasingly sensitive to inhibition of SAC over time. Aneuploid cells exhibited aberrant spindle geometry and dynamics, and kept dividing when the SAC was inhibited, resulting in the accumulation of mitotic defects, and in unstable and less-fit karyotypes. Therefore, although aneuploid cancer cells could overcome inhibition of SAC more readily than diploid cells, their long-term proliferation was jeopardized. We identified a specific mitotic kinesin, KIF18A, whose activity was perturbed in aneuploid cancer cells. Aneuploid cancer cells were particularly vulnerable to depletion of KIF18A, and KIF18A overexpression restored their response to SAC inhibition. Our results identify a therapeutically relevant, synthetic lethal interaction between aneuploidy and the SAC.
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http://dx.doi.org/10.1038/s41586-020-03114-6DOI Listing
February 2021

Global computational alignment of tumor and cell line transcriptional profiles.

Nat Commun 2021 01 4;12(1):22. Epub 2021 Jan 4.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.
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http://dx.doi.org/10.1038/s41467-020-20294-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7782593PMC
January 2021

Aneuploidy increases resistance to chemotherapeutics by antagonizing cell division.

Proc Natl Acad Sci U S A 2020 12 17;117(48):30566-30576. Epub 2020 Nov 17.

David H. Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139.

Aneuploidy, defined as whole chromosome gains and losses, is associated with poor patient prognosis in many cancer types. However, the condition causes cellular stress and cell cycle delays, foremost in G1 and S phase. Here, we investigate how aneuploidy causes both slow proliferation and poor disease outcome. We test the hypothesis that aneuploidy brings about resistance to chemotherapies because of a general feature of the aneuploid condition-G1 delays. We show that single chromosome gains lead to increased resistance to the frontline chemotherapeutics cisplatin and paclitaxel. Furthermore, G1 cell cycle delays are sufficient to increase chemotherapeutic resistance in euploid cells. Mechanistically, G1 delays increase drug resistance to cisplatin and paclitaxel by reducing their ability to damage DNA and microtubules, respectively. Finally, we show that our findings are clinically relevant. Aneuploidy correlates with slowed proliferation and drug resistance in the Cancer Cell Line Encyclopedia (CCLE) dataset. We conclude that a general and seemingly detrimental effect of aneuploidy, slowed proliferation, provides a selective benefit to cancer cells during chemotherapy treatment.
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http://dx.doi.org/10.1073/pnas.2009506117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720170PMC
December 2020

Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity.

Nat Genet 2020 11 30;52(11):1208-1218. Epub 2020 Oct 30.

Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Cultured cell lines are the workhorse of cancer research, but the extent to which they recapitulate the heterogeneity observed among malignant cells in tumors is unclear. Here we used multiplexed single-cell RNA-seq to profile 198 cancer cell lines from 22 cancer types. We identified 12 expression programs that are recurrently heterogeneous within multiple cancer cell lines. These programs are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-mesenchymal transition and protein metabolism. Most of these programs recapitulate those recently identified as heterogeneous within human tumors. We prioritized specific cell lines as models of cellular heterogeneity and used them to study subpopulations of senescence-related cells, demonstrating their dynamics, regulation and unique drug sensitivities, which were predictive of clinical response. Our work describes the landscape of heterogeneity within diverse cancer cell lines and identifies recurrent patterns of heterogeneity that are shared between tumors and specific cell lines.
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http://dx.doi.org/10.1038/s41588-020-00726-6DOI Listing
November 2020

Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action.

Nat Commun 2020 08 27;11(1):4296. Epub 2020 Aug 27.

Broad Institute of MIT and Harvard, Cambridge, 021242, MA, USA.

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment.
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http://dx.doi.org/10.1038/s41467-020-17440-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453022PMC
August 2020

Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling.

Nat Cancer 2020 Feb 20;1(2):235-248. Epub 2020 Jan 20.

Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Anti-cancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM, a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features. Our findings include compounds that killed by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, which killed via the multi-drug resistance protein ABCB1. The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation.
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http://dx.doi.org/10.1038/s43018-019-0018-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328899PMC
February 2020

Early TP53 alterations engage environmental exposures to promote gastric premalignancy in an integrative mouse model.

Nat Genet 2020 02 5;52(2):219-230. Epub 2020 Feb 5.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

Somatic alterations in cancer genes are being detected in normal and premalignant tissue, thus placing greater emphasis on gene-environment interactions that enable disease phenotypes. By combining early genetic alterations with disease-relevant exposures, we developed an integrative mouse model to study gastric premalignancy. Deletion of Trp53 in gastric cells confers a selective advantage and promotes the development of dysplasia in the setting of dietary carcinogens. Organoid derivation from dysplastic lesions facilitated genomic, transcriptional and functional evaluation of gastric premalignancy. Cell cycle regulators, most notably Cdkn2a, were upregulated by p53 inactivation in gastric premalignancy, serving as a barrier to disease progression. Co-deletion of Cdkn2a and Trp53 in dysplastic gastric organoids promoted cancer phenotypes but also induced replication stress, exposing a susceptibility to DNA damage response inhibitors. These findings demonstrate the utility of mouse models that integrate genomic alterations with relevant exposures and highlight the importance of gene-environment interactions in shaping the premalignant state.
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http://dx.doi.org/10.1038/s41588-019-0574-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031028PMC
February 2020

Next-generation characterization of the Cancer Cell Line Encyclopedia.

Nature 2019 05 8;569(7757):503-508. Epub 2019 May 8.

Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.
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http://dx.doi.org/10.1038/s41586-019-1186-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697103PMC
May 2019

WRN helicase is a synthetic lethal target in microsatellite unstable cancers.

Nature 2019 04 10;568(7753):551-556. Epub 2019 Apr 10.

Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Synthetic lethality-an interaction between two genetic events through which the co-occurrence of these two genetic events leads to cell death, but each event alone does not-can be exploited for cancer therapeutics. DNA repair processes represent attractive synthetic lethal targets, because many cancers exhibit an impairment of a DNA repair pathway, which can lead to dependence on specific repair proteins. The success of poly(ADP-ribose) polymerase 1 (PARP-1) inhibitors in cancers with deficiencies in homologous recombination highlights the potential of this approach. Hypothesizing that other DNA repair defects would give rise to synthetic lethal relationships, we queried dependencies in cancers with microsatellite instability (MSI), which results from deficient DNA mismatch repair. Here we analysed data from large-scale silencing screens using CRISPR-Cas9-mediated knockout and RNA interference, and found that the RecQ DNA helicase WRN was selectively essential in MSI models in vitro and in vivo, yet dispensable in models of cancers that are microsatellite stable. Depletion of WRN induced double-stranded DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models required the helicase activity of WRN, but not its exonuclease activity. These findings show that WRN is a synthetic lethal vulnerability and promising drug target for MSI cancers.
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http://dx.doi.org/10.1038/s41586-019-1102-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6580861PMC
April 2019

Genome-Wide Interrogation of Human Cancers Identifies EGLN1 Dependency in Clear Cell Ovarian Cancers.

Cancer Res 2019 05 21;79(10):2564-2579. Epub 2019 Mar 21.

Broad Institute of Harvard and MIT, Cambridge, Massachusetts.

We hypothesized that candidate dependencies for which there are small molecules that are either approved or in advanced development for a nononcology indication may represent potential therapeutic targets. To test this hypothesis, we performed genome-scale loss-of-function screens in hundreds of cancer cell lines. We found that knockout of , which encodes prolyl hydroxylase domain-containing protein 2 (PHD2), reduced the proliferation of a subset of clear cell ovarian cancer cell lines . EGLN1-dependent cells exhibited sensitivity to the pan-EGLN inhibitor FG-4592. The response to FG-4592 was reversed by deletion of HIF1A, demonstrating that EGLN1 dependency was related to negative regulation of HIF1A. We also found that ovarian clear cell tumors susceptible to both genetic and pharmacologic inhibition of EGLN1 required intact HIF1A. Collectively, these observations identify EGLN1 as a cancer target with therapeutic potential. SIGNIFICANCE: These findings reveal a differential dependency of clear cell ovarian cancers on EGLN1, thus identifying EGLN1 as a potential therapeutic target in clear cell ovarian cancer patients.
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http://dx.doi.org/10.1158/0008-5472.CAN-18-2674DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522283PMC
May 2019

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration.

Nat Commun 2018 11 2;9(1):4610. Epub 2018 Nov 2.

Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.
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http://dx.doi.org/10.1038/s41467-018-06916-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214982PMC
November 2018

Mutational processes shape the landscape of TP53 mutations in human cancer.

Nat Genet 2018 10 17;50(10):1381-1387. Epub 2018 Sep 17.

Dana-Farber Cancer Institute, Boston, MA, USA.

Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53 wild-type and null cell lines. We found that loss or dominant-negative inhibition of wild-type p53 function reliably enhanced cellular fitness. By integrating these data with the Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures database, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.
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http://dx.doi.org/10.1038/s41588-018-0204-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168352PMC
October 2018

Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells.

Nat Genet 2017 Dec 30;49(12):1779-1784. Epub 2017 Oct 30.

Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.
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http://dx.doi.org/10.1038/ng.3984DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5709193PMC
December 2017

Patient-derived xenografts undergo mouse-specific tumor evolution.

Nat Genet 2017 Nov 9;49(11):1567-1575. Epub 2017 Oct 9.

Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of preexisting minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Notably, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have major implications for PDX-based modeling of human cancer.
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http://dx.doi.org/10.1038/ng.3967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659952PMC
November 2017

Variability and Correlations in Primary Visual Cortical Neurons Driven by Fixational Eye Movements.

J Neurosci 2016 06;36(23):6225-41

Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20815, and

Unlabelled: The ability to distinguish between elements of a sensory neuron's activity that are stimulus independent versus driven by the stimulus is critical for addressing many questions in systems neuroscience. This is typically accomplished by measuring neural responses to repeated presentations of identical stimuli and identifying the trial-variable components of the response as noise. In awake primates, however, small "fixational" eye movements (FEMs) introduce uncontrolled trial-to-trial differences in the visual stimulus itself, potentially confounding this distinction. Here, we describe novel analytical methods that directly quantify the stimulus-driven and stimulus-independent components of visual neuron responses in the presence of FEMs. We apply this approach, combined with precise model-based eye tracking, to recordings from primary visual cortex (V1), finding that standard approaches that ignore FEMs typically miss more than half of the stimulus-driven neural response variance, creating substantial biases in measures of response reliability. We show that these effects are likely not isolated to the particular experimental conditions used here, such as the choice of visual stimulus or spike measurement time window, and thus will be a more general problem for V1 recordings in awake primates. We also demonstrate that measurements of the stimulus-driven and stimulus-independent correlations among pairs of V1 neurons can be greatly biased by FEMs. These results thus illustrate the potentially dramatic impact of FEMs on measures of signal and noise in visual neuron activity and also demonstrate a novel approach for controlling for these eye-movement-induced effects.

Significance Statement: Distinguishing between the signal and noise in a sensory neuron's activity is typically accomplished by measuring neural responses to repeated presentations of an identical stimulus. For recordings from the visual cortex of awake animals, small "fixational" eye movements (FEMs) inevitably introduce trial-to-trial variability in the visual stimulus, potentially confounding such measures. Here, we show that FEMs often have a dramatic impact on several important measures of response variability for neurons in primary visual cortex. We also present an analytical approach for quantifying signal and noise in visual neuron activity in the presence of FEMs. These results thus highlight the importance of controlling for FEMs in studies of visual neuron function, and demonstrate novel methods for doing so.
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http://dx.doi.org/10.1523/JNEUROSCI.4660-15.2016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899525PMC
June 2016

Inferring Cortical Variability from Local Field Potentials.

J Neurosci 2016 Apr;36(14):4121-35

Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20815, and

Unlabelled: The responses of sensory neurons can be quite different to repeated presentations of the same stimulus. Here, we demonstrate a direct link between the trial-to-trial variability of cortical neuron responses and network activity that is reflected in local field potentials (LFPs). Spikes and LFPs were recorded with a multielectrode array from the middle temporal (MT) area of the visual cortex of macaques during the presentation of continuous optic flow stimuli. A maximum likelihood-based modeling framework was used to predict single-neuron spiking responses using the stimulus, the LFPs, and the activity of other recorded neurons. MT neuron responses were strongly linked to gamma oscillations (maximum at 40 Hz) as well as to lower-frequency delta oscillations (1-4 Hz), with consistent phase preferences across neurons. The predicted modulation associated with the LFP was largely complementary to that driven by visual stimulation, as well as the activity of other neurons, and accounted for nearly half of the trial-to-trial variability in the spiking responses. Moreover, the LFP model predictions accurately captured the temporal structure of noise correlations between pairs of simultaneously recorded neurons, and explained the variation in correlation magnitudes observed across the population. These results therefore identify signatures of network activity related to the variability of cortical neuron responses, and suggest their central role in sensory cortical function.

Significance Statement: The function of sensory neurons is nearly always cast in terms of representing sensory stimuli. However, recordings from visual cortex in awake animals show that a large fraction of neural activity is not predictable from the stimulus. We show that this variability is predictable given the simultaneously recorded measures of network activity, local field potentials. A model that combines elements of these signals with the stimulus processing of the neuron can predict neural responses dramatically better than current models, and can predict the structure of correlations across the cortical population. In identifying ways to understand stimulus processing in the context of ongoing network activity, this work thus provides a foundation to understand the role of sensory cortex in combining sensory and cognitive variables.
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http://dx.doi.org/10.1523/JNEUROSCI.2502-15.2016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821918PMC
April 2016

Saccadic modulation of stimulus processing in primary visual cortex.

Nat Commun 2015 Sep 15;6:8110. Epub 2015 Sep 15.

Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742, USA.

Saccadic eye movements play a central role in primate vision. Yet, relatively little is known about their effects on the neural processing of visual inputs. Here we examine this question in primary visual cortex (V1) using receptive-field-based models, combined with an experimental design that leaves the retinal stimulus unaffected by saccades. This approach allows us to analyse V1 stimulus processing during saccades with unprecedented detail, revealing robust perisaccadic modulation. In particular, saccades produce biphasic firing rate changes that are composed of divisive gain suppression followed by an additive rate increase. Microsaccades produce similar, though smaller, modulations. We furthermore demonstrate that this modulation is likely inherited from the LGN, and is driven largely by extra-retinal signals. These results establish a foundation for integrating saccades into existing models of visual cortical stimulus processing, and highlight the importance of studying visual neuron function in the context of eye movements.
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http://dx.doi.org/10.1038/ncomms9110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4571196PMC
September 2015

High-resolution eye tracking using V1 neuron activity.

Nat Commun 2014 Sep 8;5:4605. Epub 2014 Sep 8.

Department of Biology, Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742, USA.

Studies of high-acuity visual cortical processing have been limited by the inability to track eye position with sufficient accuracy to precisely reconstruct the visual stimulus on the retina. As a result, studies of primary visual cortex (V1) have been performed almost entirely on neurons outside the high-resolution central portion of the visual field (the fovea). Here we describe a procedure for inferring eye position using multi-electrode array recordings from V1 coupled with nonlinear stimulus processing models. We show that this method can be used to infer eye position with 1 arc-min accuracy--significantly better than conventional techniques. This allows for analysis of foveal stimulus processing, and provides a means to correct for eye movement-induced biases present even outside the fovea. This method could thus reveal critical insights into the role of eye movements in cortical coding, as well as their contribution to measures of cortical variability.
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http://dx.doi.org/10.1038/ncomms5605DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4159777PMC
September 2014

Inferring nonlinear neuronal computation based on physiologically plausible inputs.

PLoS Comput Biol 2013 18;9(7):e1003143. Epub 2013 Jul 18.

Department of Biology and Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland, USA.

The computation represented by a sensory neuron's response to stimuli is constructed from an array of physiological processes both belonging to that neuron and inherited from its inputs. Although many of these physiological processes are known to be nonlinear, linear approximations are commonly used to describe the stimulus selectivity of sensory neurons (i.e., linear receptive fields). Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neuron's inputs. Incorporating such 'upstream nonlinearities' within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron's response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. Parameter estimation is robust and efficient even with large numbers of model components and in the context of high-dimensional stimuli with complex statistical structure (e.g. natural stimuli). We describe detailed methods for estimating the model parameters, and illustrate the advantages of the NIM using a range of example sensory neurons in the visual and auditory systems. We thus present a modeling framework that can capture a broad range of nonlinear response functions while providing physiologically interpretable descriptions of neural computation.
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http://dx.doi.org/10.1371/journal.pcbi.1003143DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3715434PMC
February 2014

Spontaneous persistent activity in entorhinal cortex modulates cortico-hippocampal interaction in vivo.

Nat Neurosci 2012 Nov 7;15(11):1531-8. Epub 2012 Oct 7.

Department of Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.

Persistent activity is thought to mediate working memory during behavior. Can it also occur during sleep? We found that the membrane potential of medial entorhinal cortex layer III (MECIII) neurons, a gateway between neocortex and hippocampus, showed spontaneous, stochastic persistent activity in vivo in mice during Up-Down state oscillations (UDS). This persistent activity was locked to the neocortical Up states with a short delay, but persisted over several cortical UDS cycles. Lateral entorhinal neurons did not show substantial persistence, and current injections similar to those used in vitro failed to elicit persistence in vivo, implicating network mechanisms. Hippocampal CA1 neurons' spiking activity was reduced during neocortical Up states, but was increased during MECIII persistent states. These results provide, to the best of our knowledge, the first direct evidence for persistent activity in MECIII neurons in vivo and reveal its contribution to cortico-hippocampal interaction that could be involved in working memory and learning of long behavioral sequences during behavior, and memory consolidation during sleep.
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http://dx.doi.org/10.1038/nn.3236DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655708PMC
November 2012

The effects of GluA1 deletion on the hippocampal population code for position.

J Neurosci 2012 Jun;32(26):8952-68

Department of Molecular Neurobiology, Max Planck Institute for Medical Research, D-69120 Heidelberg, Germany.

The GluA1 subunit of AMPA receptors (AMPARs) is critical for hippocampal synaptic transmission and plasticity. Here, we measured the activity of single units from the CA1 region of the hippocampus while GluA1 knock-out (GluA1⁻/⁻) and wild-type (WT) mice traversed a linear track. Although overall firing rates were similar, GluA1⁻/⁻ neurons were more likely to spike in bursts, but at lower burst frequencies, compared with WT neurons. GluA1⁻/⁻ neurons showed large reductions in all measures of spatial and directional selectivity compared with WT neurons. Consistent with these alterations of single-neuron properties, the accuracy of the population code for position was substantially reduced in GluA1⁻/⁻, yet it is predicted to approach the accuracy of WT with increasing population size. The absolute representation of space, independent of movement direction, was greatly diminished in GluA1⁻/⁻ mice and is predicted to remain reduced even for larger populations. Finally, we found that the rate maps of GluA1⁻/⁻ neurons showed increased trial-by-trial variability but reduced experiential plasticity compared with the WT. These results reveal the critical contribution of GluA1-containing AMPARs to individual place cells and the hippocampal population code for space, which could explain the selective behavioral impairments observed in these mice.
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http://dx.doi.org/10.1523/JNEUROSCI.6460-11.2012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3752061PMC
June 2012

Explicit-duration hidden Markov model inference of UP-DOWN states from continuous signals.

PLoS One 2011 28;6(6):e21606. Epub 2011 Jun 28.

Department of Physics, Brown University, Providence, Rhode Island, United States of America.

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021606PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125293PMC
December 2011

Speed controls the amplitude and timing of the hippocampal gamma rhythm.

PLoS One 2011 24;6(6):e21408. Epub 2011 Jun 24.

Department of Physics and Astronomy, and Integrative Center for Learning and Memory, University of California at Los Angeles, Los Angeles, California, United States of America.

Cortical and hippocampal gamma oscillations have been implicated in many behavioral tasks. The hippocampus is required for spatial navigation where animals run at varying speeds. Hence we tested the hypothesis that the gamma rhythm could encode the running speed of mice. We found that the amplitude of slow (20-45 Hz) and fast (45-120 Hz) gamma rhythms in the hippocampal local field potential (LFP) increased with running speed. The speed-dependence of gamma amplitude was restricted to a narrow range of theta phases where gamma amplitude was maximal, called the preferred theta phase of gamma. The preferred phase of slow gamma precessed to lower values with increasing running speed. While maximal fast and slow gamma occurred at coincident phases of theta at low speeds, they became progressively more theta-phase separated with increasing speed. These results demonstrate a novel influence of speed on the amplitude and timing of the hippocampal gamma rhythm which could contribute to learning of temporal sequences and navigation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021408PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123337PMC
November 2011