YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Lu, Alex X.; Zarin, Taraneh; Hsu, Ian; Moses, Alan M.

doi:10.1093/bioinformatics/btz402

Cited by 89 publications

(93 citation statements)

References 15 publications

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“…For all GFP images, seven evenly spaced z -slices covering ~6 microns in the z plane were collected for each field of view, and maximum projections of these slices were quantified for Figure 2B, or presented as micrographs in Figure 6C. To quantify basal Fus1pr-GFP expression, single cells in micrographs were segmented using YeastSpotter (http://yeastspotter.csb.utoronto.ca/) (Lu et al, 2019). The segmented masks and corresponding fluorescent images were imported into R using the ‘EBImage’ package (Pau et al, 2010), and GFP intensity for each cell was quantified using base R functions (example available on http://yeastspotter.csb.utoronto.ca).…”

Section: Methodsmentioning

confidence: 99%

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Zarin

Strome

et al. 2019

eLife

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161

152

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Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as ‘evolutionary signatures’ of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.

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Section: Methodsmentioning

confidence: 99%

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Zarin

Strome

et al. 2019

eLife

Self Cite

161

152

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“…There were many previous researches on yeast cell segmentation [39]- [41]. Different from our paper, these researches are based on the microscope, high-performance computer and large-scale laboratory equipment.…”

Section: B Analysis Of Multi-level Precision Operationmentioning

confidence: 90%

An Intelligent Low-Power Low-Cost Mobile Lab-On-Chip Yeast Cell Culture Platform

Liao

Tian

et al. 2020

IEEE Access

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Cells are the fundamental unit of life activities, and the basis of studying life phenomena. It is very important to observe the growth state of yeast cells for exploring the law of life movement, diagnosis and treatment of diseases, drug screening and so on. This study proposes a kind of intelligent low-cost portable cell culture platform using the microfluidic channel and the special machine learning circuit. The platform can independently complete the whole work of living cell culture and monitoring. For realizing the reusable and low-power deep learning circuit, a complement optimization neural network algorithm for hardware optimization and corresponding multi-clock-domain reusable multi-level precision neural network accelerator circuit were proposed, which can reduce the circuit area and power of convolution operation in all precisions by average 18.11% and 23.5% respectively. Besides, a dynamic multi-level precision control method based on the battery level is proposed to dynamically adjust the precision of machine learning operation, in order to balance the working time and segmentation accuracy of the culture platform. In addition, a microcolumns-based three-port input microfluidic structure was designed for better yeast culture effect. The experiment showed that the culture platform can realize yeast cell culture and achieve almost the same segmentation accuracy as the large biological laboratory with low-power and low-cost. Compared with the previous work, the cost of mass production was reduced by 88.95%, and the equipment volume was 27.1% smaller. At the same time, it can achieve the best balance of working time and working accuracy under the condition of limited power of equipment according to the needs of users. INDEX TERMS Microfluidic channel, yeast culture, portable cell culture platform, intelligent, low-power, lab-on-chip.

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“…Bright field images were used to segment cells using YeastSpotter (Lu et al 2019) . Cells were filtered based on circularity, solidity and the normalized difference between minor and major axis lengths to remove poorly detected cells.…”

Section: Analysis Of Microscopy Imagesmentioning

confidence: 99%

Protein context shapes the specificity of domain-peptide interactionsin vivo

Dionne

Bourgault

Dubé

et al. 2020

Preprint

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SRC Homology 3 (SH3) domains contribute to cellular processes via their ability to support protein-protein interactions (PPIs). While the intrinsic binding specificities of SH3 domains have been studied in vitro , whether each domain is necessary and sufficient to define PPI specificity in vivo is largely unknown. We combine genome editing, deep mutagenesis scanning, PPI mapping, growth phenotyping and single cell imaging in yeast to identify SH3-dependent PPI networks and their associated phenotypes. We show that both the sequence of the SH3 host protein and the position of the SH3 domains within their host are critical for interaction specificity and for cellular processes such as endocytosis. We further validate these findings using a human multi-SH3 adaptor protein and investigating its ability to promote phase separation. Our work highlights the importance of the interplay between a SH3 domain and its host protein for the regulation of cellular and biophysical processes.

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YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Cited by 89 publications

References 15 publications

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

An Intelligent Low-Power Low-Cost Mobile Lab-On-Chip Yeast Cell Culture Platform

Protein context shapes the specificity of domain-peptide interactionsin vivo

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