Label-free nanoscopy of cell metabolism by ultrasensitive reweighted visible stimulated Raman scattering.

Super-resolution imaging of cell metabolism is hindered by the incompatibility of small metabolites with fluorescent dyes and the limited resolution of imaging mass spectrometry. We present ultrasensitive reweighted visible stimulated Raman scattering (URV-SRS), a label-free vibrational imaging technique for multiplexed nanoscopy of intracellular metabolites. We developed a visible SRS microscope with extensive pulse chirping to improve the detection limit to ~4,000 molecules and introduced a self-supervised multi-agent denoiser to suppress non-independent noise in SRS by over 7.

Dynamic heterogeneity in an stress response regulon mediates gene activation and antimicrobial peptide tolerance.

The bacterial stress response is an intricately regulated system that plays a critical role in cellular resistance to drug treatment. The complexity of this response is further complicated by cell-to-cell heterogeneity in the expression of bacterial stress response genes. These genes are often organized into networks comprising one or more transcriptional regulators that control expression of a suite of downstream genes.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring mechanistic knowledge, are an ideal tool for this task.

Longitudinal Single-Cell Imaging of Engineered Strains with Stimulated Raman Scattering to Characterize Heterogeneity in Fatty Acid Production.

Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single-cell level over time. Here, longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging is developed to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. Compositional analysis is also developed to estimate the chain length and unsaturation of the fatty acids in living cells.

Dynamic gene expression and growth underlie cell-to-cell heterogeneity in stress response.

Cell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is important because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth.

Anticipating antibiotic resistance.

Machine learning can use clinical history to lower the risk of infection recurrence.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.

Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning.

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400-1800 cm) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples.

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field.

Cell-machine interfaces for characterizing gene regulatory network dynamics.

Gene regulatory networks and the dynamic responses they produce offer a wealth of information about how biological systems process information about their environment. Recently, researchers interested in dissecting these networks have been outsourcing various parts of their experimental workflow to computers. Here we review how, using microfluidic or optogenetic tools coupled with fluorescence imaging, it is now possible to interface cells and computers.

Identification of individual cells from z-stacks of bright-field microscopy images.

Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features.

Balancing a genetic toggle switch by real-time feedback control and periodic forcing.

Cybergenetics is a novel field of research aiming at remotely pilot cellular processes in real-time with to leverage the biotechnological potential of synthetic biology. Yet, the control of only a small number of genetic circuits has been tested so far. Here we investigate the control of multistable gene regulatory networks, which are ubiquitously found in nature and play critical roles in cell differentiation and decision-making.

Extracellular Vesicle Production Loaded with Nanoparticles and Drugs in a Trade-off between Loading, Yield and Purity: Towards a Personalized Drug Delivery System.

Extracellular vesicles (EVs) released by cells and circulating in body fluids are recognized as potent vectors of intercellular self-communication. Due to their cellular origin, EVs hold promise as naturally targeted "personalized" drug delivery system insofar as they can be engineered with drugs or theranostic nanoparticles. However, technical hurdles related to their production, drug loading, purification, and characterization restrain the translation of self-derived EVs into a clinical drug delivery system.

Noise propagation in synthetic gene circuits for metabolic control.

Dynamic control of enzyme expression can be an effective strategy to engineer robust metabolic pathways. It allows a synthetic pathway to self-regulate in response to changes in bioreactor conditions or the metabolic state of the host. The implementation of this regulatory strategy requires gene circuits that couple metabolic signals with the genetic machinery, which is known to be noisy and one of the main sources of cell-to-cell variability.

[The value of laparoscopy in the staging of digestive system cancers].

Sixty two patients with a gastrointestinal carcinoma were evaluated pre-operatively by ultrasonography, CT scan and laparoscopy to seek liver metastases and/or peritoneal carcinomatosis. Performance levels of laparoscopy, ultrasonography and CT scan were comparable regarding the diagnosis of liver metastases. Laparoscopy was markedly better than CT scan and ultrasonography in the diagnosis of peritoneal carcinomatosis.