scholarly journals Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

2021 ◽  
Author(s):  
Andreas P. Cuny ◽  
Aaron Ponti ◽  
Tomas Kuendig ◽  
Fabian Rudolf ◽  
Joerg Stelling
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Cell Tracking ◽  
Structural Information ◽  
Experimental Studies ◽  
Size Control ◽  
Population Level ◽  
Distance Measures ◽  
Cell Region ◽  
Low Frequencies

Experimental studies of cell growth, inheritance, and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking - assigning identical cells on consecutive images to a track - is often challenging due to imperfect segmentation, moving cells, or focus drift, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance measures the similarity between cells in two consecutive images by comparing the structural information contained in the low frequencies of a Fourier transform. We show that it is broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses the concept to reject unlikely assignments, thereby substantially increasing tracking performance on published and newly generated long-term data sets from various species. For S.cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator. It demonstrates how highly precise tracking can help uncover previously undescribed single-cell biology.

scholarly journals Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

10.3791/56291 ◽  
2017 ◽  
Author(s):  
Rosa Gómez-Villafuertes ◽  
Lucía Paniagua-Herranz ◽  
Sergio Gascon ◽  
David de Agustín-Durán ◽  
María de la O Ferreras ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Cell Tracking ◽  
Live Imaging ◽  
Neural Populations

scholarly journals Phototaxis of the dominant marine pico-eukaryote Micromonas sp.: from population to single cell

2019 ◽  
Author(s):  
Richard Henshaw ◽  
Raphaël Jeanneret ◽  
Marco Polin
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Population Level ◽  
Qualitative Description ◽  
Drift Diffusion ◽  
Population Responses ◽  
Wide Range ◽  
Primary Producer ◽  
Run And Tumble ◽  
Micromonas Pusilla

Micromonas commoda (previously Micromonas pusilla, a unicellular photosynthetic picoeukaryote globally dominant in marine ecosystems, has previously been qualified as being strongly phototactic. To date, no detailed quantitative or qualitative description of this behaviour has been reported, nor have thorough studies of its motility been undertaken. This primary producer has only been qualitatively described as utilizing run-and-tumble motion, but such motility strategy is incompatible with its morphology comprising only one propelling flagellum. Moreover, it is still unclear as to how Micromonas sp. detects a light direction due to the lack of a dedicated eyespot; the organism is essentially blind. Here we first perform population-scale phototactic experiments to show that this organism actively responds to a wide range of light wavelengths and intensities. These population responses are well accounted for within a simple drift-diffusion framework. Based on single-cell tracking experiments, we then detail thoroughly Micromonas sp.’s motility which resembles run-and-reverse styles of motion commonly observed in marine prokaryotes and that we name stop-run or reverse. The associated peculiar microscopic changes upon photo-stimulation are finally described and integrating those into jump-diffusion simulations appears to produce phototactic drifts that are quantitatively compatible with those obtained experimentally at the population level.

scholarly journals Responses of Vertebrate Wildlife to Oil and Natural Gas Development: Patterns and Frontiers

2021 ◽  
Author(s):  
A. D. Chalfoun
Keyword(s):  
Natural Gas ◽  
Anthropogenic Activities ◽  
Spatial Scales ◽  
Habitat Type ◽  
Population Level ◽  
Low Frequencies ◽  
Natural Gas Development ◽  
The Usa ◽  
Oil And Natural Gas

Abstract Purpose of Review Anthropogenic activities can lead to the loss, fragmentation, and alteration of wildlife habitats. I reviewed the recent literature (2014–2019) focused on the responses of avian, mammalian, and herpetofaunal species to oil and natural gas development, a widespread and still-expanding land use worldwide. My primary goals were to identify any generalities in species’ responses to development and summarize remaining gaps in knowledge. To do so, I evaluated the directionality of a wide variety of responses in relation to taxon, location, development type, development metric, habitat type, and spatiotemporal aspects. Recent Findings Studies (n = 70) were restricted to the USA and Canada, and taxonomically biased towards birds and mammals. Longer studies, but not those incorporating multiple spatial scales, were more likely to detect significant responses. Negative responses of all types were present in relatively low frequencies across all taxa, locations, development types, and development metrics but were context-dependent. The directionality of responses by the same species often varied across studies or development metrics. Summary The state of knowledge about wildlife responses to oil and natural gas development has developed considerably, though many biases and gaps remain. Studies outside of North America and that focus on herpetofauna are lacking. Tests of mechanistic hypotheses for effects, long-term studies, assessment of response thresholds, and experimental designs that isolate the effects of different stimuli associated with development, remain critical. Moreover, tests of the efficacy of habitat mitigation efforts have been rare. Finally, investigations of the demographic effects of development across the full annual cycle were absent for non-game species and are critical for the estimation of population-level effects.

scholarly journals Optical and genetic tools for in vivo single cell tracking

2021 ◽  
Vol 358 ◽  
pp. 109192
Author(s):  
Yajie Liang ◽  
Liset M. de la Prida
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Genetic Tools

Statistical mechanics meets single-cell biology

2021 ◽  
Author(s):  
Andrew E. Teschendorff ◽  
Andrew P. Feinberg
Keyword(s):  
Statistical Mechanics ◽  
Single Cell ◽  
Cell Biology

scholarly journals Single-cell biology to decode the immune cellular composition of kidney inflammation

2021 ◽  
Author(s):  
Yu Zhao ◽  
Ulf Panzer ◽  
Stefan Bonn ◽  
Christian F. Krebs
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Computational Analysis ◽  
Immune Cell ◽  
Therapeutic Interventions ◽  
Experimental Setup ◽  
Disease Etiology ◽  
Rna Profiling ◽  
Immune Mediated ◽  
Health And Disease

AbstractSingle-cell biology is transforming the ability of researchers to understand cellular signaling and identity across medical and biological disciplines. Especially for immune-mediated diseases, a single-cell look at immune cell subtypes, signaling, and activity might yield fundamental insights into the disease etiology, mechanisms, and potential therapeutic interventions. In this review, we highlight recent advances in the field of single-cell RNA profiling and their application to understand renal function in health and disease. With a focus on the immune system, in particular on T cells, we propose some key directions of understanding renal inflammation using single-cell approaches. We detail the benefits and shortcomings of the various technological approaches outlined and give advice on potential pitfalls and challenges in experimental setup and computational analysis. Finally, we conclude with a brief outlook into a promising future for single-cell technologies to elucidate kidney function.

scholarly journals Melanoma Single-Cell Biology in Experimental and Clinical Settings

2021 ◽  
Vol 10 (3) ◽  
pp. 506
Author(s):  
Hans Binder ◽  
Maria Schmidt ◽  
Henry Loeffler-Wirth ◽  
Lena Suenke Mortensen ◽  
Manfred Kunz
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Intracellular Signaling ◽  
Cell Biology ◽  
Immune Cell ◽  
Malignant Tumors ◽  
Checkpoint Inhibitor ◽  
Treatment Resistance ◽  
Resistance Mechanisms ◽  
Cellular Heterogeneity

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.

scholarly journals Graph Representation Learning for Single Cell Biology

2021 ◽  
Author(s):  
Leon Hetzel ◽  
David S. Fischer ◽  
Stephan Günnemann ◽  
Fabian J. Theis
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Representation Learning ◽  
Graph Representation
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