Jean-Baptiste Lugagne

• Boston University

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 m...

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where lar...

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...

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...

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 Escher...

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where lar...

Significance Individual bacteria that share identical genomes and growth environments can display substantial cell-to-cell differences in expression of stress-response genes and single-cell growth rates. This phenotypic heterogeneity can impact the survival of single cells facing sudden stress. However, the windows of time that cells spend in vulne...

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

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...

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...

Engineered microbes can produce valuable chemicals, however production strains often require extensive optimization before they can be used at industrial scales. To quantify production, metabolic engineers typically employ mass spectrometry, which offers excellent chemical specificity. However, with this approach samples are derived from bulk cultu...

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−1) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral...

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 critical because dramatically different outcomes...

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 focus...

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions in living systems. Specifically, SRS in the fingerprint region can resolve multiple chemicals in a complex bio-environment using specific and well-separated Raman signatures. Yet, fingerprint SRS imaging with micr...

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human supervision 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 foc...

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...

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 informa...

Cybergenetics is a novel field of research aiming at remotely piloting cellular processes in real-time 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 fo...

This report presents the fabrication of bifunctional magnetic and fluorescent microneedles (µNDs) made of a ternary mixture of magnetic nanoparticles (NPs), quantum dots (QDs), and polyelectrolyte. The assembly relies on the electrostatic complexation of negatively charged NPs with positively charged polymer strands and is controlled by the charge...

The human body possesses its own outperforming delivery device in extracellular vesicles that circulate in all body fluids and act as a farreaching intercellular communication pathway. Engineering extracellular vesicles with therapeutic and imaging agents opens up unprecedented perspectives with a new generation of 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 hur...

Les progrès récents de la microfluidique, la biologie synthétique, la microscopie automatisée rendent aujourd'hui possible le contrôle externe de l'expression des gènes en temps réel. Parmi les défis que devra relever le domaine du contrôle externe et temps-réel de l'expression des gènes se trouve la possibilité de contrôler des réseaux de régulati...

Recent progresses in microfluidics, synthetic biology and microscopy automation now make it possible to control gene expression externally and in real time. Among the challenges facing the field of external real-time control of gene expression is the control of intricate, multistable gene regulation networks as well as the control of several target...

Dynamic control of enzyme expression can be an effective strategy to engineer robust metabolic pathways. Dynamic control 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 w...

The interaction between gene expression and metabolism is a form of feedback control that allows cells to up- or downregulate specific reactions according to the environmental conditions. Although gene expression is an inherently stochastic process, the effect of genetic feedback on the propagation of noise to the metabolic layer remains largely un...