Takamasa Kudo

Takamasa Kudo
Stanford University | SU · Department of Chemical and Systems Biology

About

73
Publications
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2,575
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Publications

Publications (73)
Article
Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting pathogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the transcription factor hypoxia-inducible factor 1α (HIF-1α). Single-cell dynamics have been shown to be an important de...
Article
Pooled genetic libraries have improved screening throughput for mapping genotypes to phenotypes. However, selectable phenotypes are limited, restricting screening to outcomes with a low spatiotemporal resolution. Here, we integrated live-cell imaging with pooled library-based screening. To enable intracellular multiplexing, we developed a method ca...
Article
Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to...
Article
Full-text available
A Correction to this paper has been published: https://doi.org/10.1038/s41592-021-01059-w
Article
Full-text available
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we iden...
Article
Half of the bacteria in the human gut microbiome are lysogens containing integrated prophages, which may activate in stressful immune environments. Although lysogens are likely to be phagocytosed by macrophages, whether prophage activation occurs or influences the outcome of bacterial infection remains unexplored. To study the dynamics of bacteria-...
Preprint
Full-text available
Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning...
Article
Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning...
Article
Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analys...
Article
Over the last decade, multiple studies have shown that signaling proteins activated in different temporal patterns, such as oscillatory, transient, and sustained, can result in distinct gene expression patterns or cell fates. However, the molecular events that ensure appropriate stimulus- and dose-dependent dynamics are not often understood and are...
Article
Full-text available
Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics...
Article
During an infection, immune cells must identify the specific level of threat posed by a given bacterial input in order to generate an appropriate response. Given that they use a general non-self-recognition system, known as Toll-like receptors (TLRs), to detect bacteria, it remains unclear how they transmit information about a particular threat. To...
Article
Deep learning is transforming the ability of life scientists to extract information from images. These techniques have better accuracy than conventional approaches and enable previously impossible analyses. As the capability of deep learning methods expands, they are increasingly being applied to large imaging datasets. The computational demands of...
Article
Although kinases are important regulators of many cellular processes, measuring their activity in live cells remains challenging. We have developed kinase translocation reporters (KTRs), which enable multiplexed measurements of the dynamics of kinase activity at a single-cell level. These KTRs are composed of an engineered construct in which a kina...
Article
Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these que...
Article
Full-text available
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual c...
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for E. coli. (TIF)
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for MCF10A cells. (TIF)
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for NIH-3T3 cells. (TIF)
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for semantic segmentation of MCF10A cells and NIH-3T3 cells. (TIF)
Data
Additional semantic segmentation of NIH-3T3 and MCF10A cells. 286 cells were analyzed, including 93 3T3 cells and 192 MCF 10A cells. The classification accuracy was 89% for NIH-3T3 cells and 98% for MCF10A cells. (TIF)
Data
Sensitivity analysis of the influence of the cytoring size on the dynamics of the JNK-KTR. While the qualitative shapes of the curves remain intact as the size of the cytoring increases, there are important quantitative differences that emerge as the cytoring increases. In the grey trace, the first peak is identified as being quantitatively identic...
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for RAW 264.7 cells. (TIF)
Data
Segmentation accuracy vs. cell density for HeLa-S3 cells. 1250, 2500, 5000, 10000, and 20000 cells were plated in the wells of a 96 well dish and imaged. The images were segmented manually and with conv-nets to compute the Jaccard and Dice indices. Segmentation performance remains stable for most plating densities, but decreases once cells are conf...
Data
Bacteria-net output when used to process S1 Movie. (AVI)
Data
Nuclear marker channel for S3 Movie. (AVI)
Data
Representative movie of HeLa-S3 with overlaying nuclear marker and segmentation boundaries. (AVI)
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for H2B-iRFP labeled and DAPI stained nuclei. (TIF)
Data
Poorly performing conv-nets. This figure highlights the importance of image normalization and receptive field size in training robust conv-nets. (a), (b), and (c). Mammalian-net semantic was trained on an un-normalized images of NIH-3T3 and MCF10A cells. Instead of learning differences in cell shape, the conv-net learned the brightness difference b...
Data
Phase images of HeLa-S3 cells expressing the JNK-KTR. (AVI)
Data
JNK-KTR channel for S3 Movie. (AVI)
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for HeLa-S3 cells. (TIF)
Data
Sample image, conv-net interior prediction map, segmentation mask, and training/validation error curves for bone marrow derived macrophages. (TIF)
Data
Areas of the different cytorings used in S10 Fig. (TIF)
Data
Histogram of the instantaneous growth rate for a bacterial micro-colony. This histogram is identical to the histogram showed in Fig 3, with the axes expanded to show the negative growth rates corresponding to cell division. (TIF)
Data
Regularization optimization. The L2 regularization parameter was varied from 0, 10−7, 10−6, and 10−5. Lower regularization was associated with more fluctuations in the classification error on the validation data set. (TIF)
Data
Phase images of a growing E. coli micro colony. (AVI)
Data
Training and validation error for conv-nets to assess the performance improvements provided by dropout, batch normalization, shearing for data augmentation, and multi-resolution fully connected layers. Dropout was only used in fully connected layers. The segmentation performance of each network as quantified by the Jaccard and Dice indices is provi...
Data
Comparison of segmentation performance of conv-nets and Ilastik on a HeLa cell validation data set. (a) Phase image of HeLa cells. (b) Ground truth for the cell boundary. (c) Conv-net soft-max score for edge and interior prediction. (d) Ilastik segmentation. (e) Conv-net segmentation after active contour processing. (f) Ilastik segmentation after a...
Article
Full-text available
Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combina...
Data
Boxplots of Residuals of LOO CVs. (A) For each IEGs, a boxplot of residual distribution against each round of LOO CV dataset over seven conditions is shown in a panel. A red line, blue box and whisker indicate the median, the interquartile range (IQR), the end point of data point, which is not outlier. A red marker + indicates the outlier. A data p...
Data
The mean square error of training data set and leave one out error of cross validation of the four models. Blue, c-FOS; cyan, EGR1; green, c-JUN; orange, JUNB; red, FOSB. Ordinary FIR, a finite impulse response model combined with ordinary regression; FIR-PLS, a finite impulse response model combined with partial least square (PLS) regression; ordi...
Data
pMAPKs and pCREB as inputs, and immediate early gene expression as outputs. (A) Time series of responses of pMAPKs and pCREB (circles) to NGF (0.5 ng/ml, red), PACAP (1 nM, blue), or EGF (0.5 ng/ml, green). Responses were measured by QIC at 3-min intervals over a total period of 180 min. (B) Time series showing the expression of immediate early gen...
Data
Loadings and scores for the inputs and outputs. In all panels, the vertical axis is the 1st principal component (PC1) and the horizontal axis is the 2nd principal component (PC2). For the panels showing input and output scores, a red circle indicates 5ng/ml NGF; red diamond, 0.5 ng/ml NGF; blue circle, 100 nM PACAP; blue diamond, 1 nM PACAP; green...
Data
The mean square error (MSE) of the training data set. (TIF)
Data
The mean square error (MSE) of leave one out error of cross validation (LOO CV). (TIF)
Data
Boxplots of coefficient parameters of LOO CV and parameters of the final LF-PLS model. For each regression coefficients, distribution over LOO CVs are plotted as a box plot. The parameters from the final model that were trained with all the datasets are shown as green circles. (TIF)
Data
The p-values of the Mann—Whitney U-test for squared residuals between LF-PLS and FIR-PLS are shown. (TIF)
Data
The boxplot of squared residuals of LOO CV. (A) For each IEGs, the squared residual of LF-PLS and FIR-PLS against LOC CVs over seven conditions are shown by boxplot. (B) For each IEGs, the logarithm of squared residual of each model against LOC CVs over seven conditions is shown by boxplot. (TIF)
Data
The IO relationships between signaling molecules and IEGs estimated by VIP score. Black solid line, gray solid line, dashed line are the IO relationship estimated by both VIP score and AIC of nonlinear ARX model, only VIP score, and only AIC of nonlinear ARX model, respectively. Dotted line is IO relationship estimated by AIC of nonlinear ARX model...
Article
Full-text available
Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphoryl...
Article
Full-text available
The accumulation of protein aggregates is a common pathological hallmark of many neurodegenerative diseases. However, we do not fully understand how aggregates are formed or the complex network of chaperones, proteasomes and other regulatory factors involved in their clearance. Here, we report a chemically controllable fluorescent protein that enab...
Data
Time-lapse images of representative HEK cells stably expressing AgDD. Cells cultured in media with S1 throughout the experiment.
Data
Time-lapse images of representative HEK cells stably expressing AgDD. Cells cultured in media with S1 withdrawn from 0-1 h then readministered from 1-8 h.
Data
Early time-lapse images of representative HEK293 cells stably expressing Nuclear AgDD following S1 removal.
Data
Time-lapse images of representative HEK cells stably expressing AgDD. Cells cultured in media with S1 withdrawn at 0 h.
Data
Early time-lapse images of representative HEK cells stably expressing AgDD following S1 withdrawal.
Data
Time-lapse images of representative HEK293 cells stably expressing Nuclear AgDD. Cells cultured in media with S1 throughout the experiment.
Data
Time-lapse images of representative HEK293 cells stably expressing Nuclear AgDD. Cells cultured in media with S1 withdrawn at 0 h.
Preprint
Cellular signalling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signalling systems remain poorly understood. Here we measure the temporal evolution of...
Article
Mammalian tissue size is maintained by slow replacement of aging cells. For adipocytes, key regulators of glucose and lipid metabolism, the renewal rate is only 10% per year. Here we use computational modeling, quantitative mass spectrometry (1) and single‐cell microscopy (2) to show that expression noise acts within a network of more than six posi...
Article
Full-text available
Mammalian tissue size is maintained by slow replacement of de-differentiating and dying cells. For adipocytes, key regulators of glucose and lipid metabolism, the renewal rate is only 10% per year. We used computational modeling, quantitative mass spectrometry, and single-cell microscopy to show that cell-to-cell variability, or noise, in protein a...
Article
Proteins detrimental to ER morphology need to be efficiently exported. Here, we identify two mechanisms controlling trafficking of Arabidopsis thaliana GLL23, a 43 kDa GDSL-like lipase implicated in glucosinolate metabolism through its association with the β-glucosidase myrosinase. Using immunofluorescence, we identified two mutants showing aberran...
Article
Full-text available
Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been...
Article
Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population...

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