Biology subjects

Pfister, H.

Publications and source records attributed to Pfister, H..

33 records · Page 2Linked to original sources

bioRxiv2022

Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Lymphocytes play a key role in immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. Here, we used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of tumor-bearing lung tissues from a Kras/p53 (KP) mouse model and human resections. Networks of directly interacting lymphocytes ( lymphonets) emerge as a distinctive feature of the anti-cancer immune response. Lymphonets nucleate from small T-cell clusters and incorporate B cells with increasing size. CXCR3-mediated trafficking modulates lymphonet size and number, but neoantigen expression directs intratumoral localization. Lymphonets preferentially harbor TCF1+/PD1+ progenitor CD8 T cells involved in responses to immune checkpoint blockade (ICB). Upon treatment of mice with ICB therapy or a neoantigen-targeted vaccine, lymphonets retain progenitor and gain cytotoxic CD8 T-cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8 T-cell anti-tumor responses.

cancer biology↗

bioRxiv2022

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

cell biology↗

bioRxiv2022

Saturated reconstruction of living brain tissue

Complex wiring between neurons underlies the information-processing network enabling all brain functions, including cognition and memory. For understanding how the network is structured, processes information, and changes over time, comprehensive visualization of the architecture of living brain tissue with its cellular and molecular components would open up major opportunities. However, electron microscopy (EM) provides nanometre-scale resolution required for full in-silico reconstruction1-5, yet is limited to fixed specimens and static representations. Light microscopy allows live observation, with super-resolution approaches6-12 facilitating nanoscale visualization, but comprehensive 3D-reconstruction of living brain tissue has been hindered by tissue photo-burden, photobleaching, insufficient 3D-resolution, and inadequate signal-to-noise ratio (SNR). Here we demonstrate saturated reconstruction of living brain tissue. We developed an integrated imaging and analysis technology, adapting stimulated emission depletion (STED) microscopy6,13 in extracellularly labelled tissue14 for high SNR and near-isotropic resolution. Centrally, a two-stage deep-learning approach leveraged previously obtained information on sample structure to drastically reduce photo-burden and enable automated volumetric reconstruction down to single synapse level. Live reconstruction provides unbiased analysis of tissue architecture across time in relation to functional activity and targeted activation, and contextual understanding of molecular labelling. This adoptable technology will facilitate novel insights into the dynamic functional architecture of living brain tissue.

neuroscience↗

bioRxiv2021

A connectomic study of a petascale fragment of human cerebral cortex

We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at [~]30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each others initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to [~]20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.

neuroscience↗

bioRxiv2021

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation, a challenging problem that has recently benefited from the use of deep learning. In this paper, we demonstrate two approaches to improving tissue segmentation that are applicable to multiple deep learning frameworks. The first uses "real augmentations" that comprise defocused and saturated image data collected on the same instruments as the actual data; using real augmentation improves model accuracy to a significantly greater degree than computational augmentation (Gaussian blurring). The second involves imaging the nuclear envelope to better identify nuclear outlines. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types and provide a set of improved segmentation models. We speculate that the use of real augmentations may have applications in image processing outside of microscopy.

bioinformatics↗

bioRxiv2020

The Wood Image Analysis and Dataset (WIAD): open-access visual analysis tools to advance the ecological data revolution

O_LIEcological data is collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring-width measurements before they can be shared in various common file formats. Archiving digital images of wood samples in libraries, that have been developed for ecological analysis and are publicly available, remains the exception. C_LIO_LIWe developed the Wood Image Analysis and Dataset (WIAD) an open-source application including a web-interface to integrate basic visual analysis of wood samples, such as increment cores, thin sections or x-ray films, basic data processing, and archiving of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood. C_LIO_LIWIAD provides user-friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early- and latewood widths, and fire scars, and to visualise, process, and archive images, metadata, and the derived data. C_LIO_LIWIAD constitutes a step towards the reproducible automation of tree-ring analysis, while establishing an open-source foundation to create improved community developed repositories which would enable novel ecological studies harnessing the wealth of existing visual data. C_LI

plant biology↗

bioRxiv2020

Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser

The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or "digital docents" that allow experts to help make complex images intelligible. We illustrate this idea using Minerva software and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.

scientific communication and education↗

bioRxiv2020

Channel Embedding for Informative Protein Identification from Highly Multiplexed Images

Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information. For instance, highly multiplexed tumor and tissue images have 30-100 channels and require interpretation methods that work across many channels to provide deep molecular insights. We propose a novel channel embedding method that extracts features from each channel. We then use these features to train a classifier for prediction. Using this channel embedding, we apply an interpretation method to rank the most discriminative channels. To validate our approach, we conduct an ablation study on a synthetic dataset. Moreover, we demonstrate that our method aligns with biological findings on highly multiplexed images of breast cancer cells while outperforming baseline pipelines.

pathology↗

bioRxiv2019

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109 or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

systems biology↗

bioRxiv2019

The rich get richer: synaptic remodeling between climbing fibers and Purkinje cells in the developing cerebellum begins with positive feedback addition of synapses.

During postnatal development, cerebellar climbing fibers strongly innervate a subset of their original Purkinje cell targets and eliminate their connections from the rest. In the adult, each climbing fiber innervates a small number of Purkinje cells and each Purkinje cell is innervated by a single climbing fiber. To get insight about the processes responsible for this remapping, we reconstructed serial electron microscopy datasets from mice during the first postnatal week. In contrast to adult connectivity, individual neonatal climbing fibers innervate many nearby Purkinje cells, and multiple climbing fibers innervate each Purkinje cell. Between postnatal days 3 and 7, Purkinje cells retract long dendrites and grow many proximal dendritic processes. On this changing landscape, individual climbing fibers selectively add many synapses to a subset of Purkinje cell targets in a positive-feedback manner, without pruning synapses from other Purkinje cells. The active zone sizes of synapses associated with powerful versus weak inputs are indistinguishable. These results show that changes in synapse number rather than synapse size are the predominant form of early developmental plasticity. Finally, although multiple climbing fibers innervate each Purkinje cell in early postnatal development, the number of climbing fibers and Purkinje cells in a local cerebellar region nearly match. Thus, initial over-innervation of Purkinje cells by climbing fibers is economical, in that the number of axons entering a region is enough to assure that each axon ends up with a postsynaptic target, and that none branched there in vain.\n\nHIGHLIGHTSO_LIDeveloping climbing fibers establish synapses on many neighboring Purkinje cells unlike the sparse pattern of innervation in later life\nC_LIO_LIClimbing fibers add many synapses onto a few of their Purkinje targets before the pruning stage in a rich-get-richer type process\nC_LIO_LIThe synapse sizes of strengthened and weakened climbing fiber inputs are indistinguishable.\nC_LIO_LIExuberant branching of climbing fiber axons in early postnatal life appears to be economical because the numbers of axons and Purkinje cells in a local region match, ensuring that each axon can establish a long-lasting connection there\nC_LI\n\nBLURBHigh-resolution serial electron microscopy reconstructions reveal that climbing fiber-Purkinje cell synaptic refinement in the developing cerebellum begins with significant synapse addition. Climbing fibers focus their synapses onto a smaller number of Purkinje cells by selectively adding synapses onto some target cells. All axons that project to a region in development play a role in the final connectivity.

neuroscience↗

bioRxiv2019

Peax: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning

We present PO_SCPLOWEAXC_SCPLOW, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the users perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users perceived similarity. Using an active learning sampling strategy, PO_SCPLOWEAXC_SCPLOW collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate PO_SCPLOWEAXC_SCPLOWs features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of PO_SCPLOWEAXC_SCPLOW. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

genomics↗

bioRxiv2018

Pattern-Driven Navigation in 2D Multiscale Visual Spaces with Scalable Insets

We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visualizations such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visualizations is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the annotated patterns. Insets support users in searching, comparing, and contextualizing patterns while reducing the amount of navigation needed. They are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated patterns are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. In a controlled user study with 18 participants, we found that Scalable Insets can speed up visual search and improve the accuracy of pattern comparison at the cost of slower frequency estimation compared to a baseline technique. A second study with 6 experts in the field of genomics showed that Scalable Insets is easy to learn and provides first insights into how Scalable Insets can be applied in an open-ended data exploration scenario.

bioinformatics↗

bioRxiv2017

ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms’ proteomes

MotivationProtein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the S. cerevisiae and E. coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution is studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level.\n\nResultsWe demonstrate the utility and versatility of ProteomeVis by investigating the distribution of edges per node in organismal protein chain universe graphs (oPCUGs) and putative ER determinants. S. cerevisiae and E. coli oPCUGs are scale-free with scaling constants of 1.78 and 1.50, respectively. Both scaling constants can be explained by a previously reported theoretical model describing protein structure evolution (Dokholyan et al., 2002). Protein abundance most strongly correlates with ER among properties in ProteomeVis, with Spearman correlations of -0.51 (p-value<10-10) and -0.46 (p-value<10-10) for S. cerevisiae and E. coli, respectively. This result is consistent with previous reports that found protein expression to be the most important ER determinant (Zhang and Yang, 2015).\n\nAvailabilityProteomeVis is freely accessible at http://proteomevis.chem.harvard.edu/proteomevis/.\n\nContactshakhnovich@chemistry.harvard.edu\n\nSupplementary informationAvailable to download on bioRxiv.

evolutionary biology↗

bioRxiv2017

HiPiler: Visual Exploration Of Large Genome Interaction Matrices With Interactive Small Multiples

This paper presents an interactive visualization interface--HiPiler--for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like \"snippets\". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

bioinformatics↗

bioRxiv2017

HiGlass: Web-based Visual Comparison And Exploration Of Genome Interaction Maps

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.

bioinformatics↗