Biology subjects

Rosenstein, J. K.

Publications and source records attributed to Rosenstein, J. K..

5 recordsLinked to original sources

bioRxiv2026

Label-Free All-Electrical Tracking of Individual and Collective Cell Migration on a Megapixel CMOS Capacitance Sensor

Label-free tracking of adherent cell migration could enable important insights into biological processes such as tissue repair, inflammatory response, or cancer progression. Nevertheless, visualizing unlabeled animal cells using optical microscopy remains challenging due to low contrast as well as frequent changes in cell shape and number. A promising alternative uses electrical capacitance measurements, which are sensitive to cell adhesion to electrode surfaces. However, prior examples often utilized electrodes with areas larger than single cells, resulting in averaged readouts over multiple cells. Here, we demonstrate label-free, live-cell tracking using a capacitance sensor array with more than 1 million pixels on a 10 micron pitch across an area larger than 1 square centimeter. We show that single cell morphology can be clearly segmented, and then used to reconstruct migration and proliferation dynamics using optical flow. We further track the spreading of multicellular spheroids, revealing fast-moving peripheral regions led by a collective leader cell "front." Finally, we demonstrate label-free imaging of millimeter-scale honeycomb-shaped tissues without the multi-image stitching often required for conventional microscopy. We utilize mutual capacitance measurements with electrically-programmable electrode spacing to reconstruct topographical features of these engineered tissues. Overall, CMOS capacitance imaging arrays enables label-free imaging spanning from single cells to large tissues, in a portable and scalable format for settings where optical microscopy may be difficult to access.

bioengineering↗

bioRxiv2024

Non-Optical, Label-free Electrical Capacitance Imaging of Microorganisms

Many fundamental insights into microbiology have come from imaging, which is typically synonymous with optical techniques. However, the sample preparation needed for many optical microscopy methods such as labeling, fixing, or genetic modification, limits the range of species and environments we can investigate. Here we demonstrate the use of electrical capacitance measurements as a non-optical method for imaging live microbial samples. In electrical capacitance imaging (ECI), samples are positioned in contact with a semiconductor sensor array, and localized capacitance measurements are made across the array. From these measurements, we generate textured images of a variety of microbial colonies. We determine that capacitance is correlated with local sample thickness by comparing ECI data to 3D confocal scans. We further illustrate with ECI that a difference in capacitance signal allows microbial species to be spatially distinguished in co-culture conditions. In order to highlight the versatility of our system, we capture the cross-sectional development of floating pellicle biofilms in a liquid culture at millimeter length scales during weeks-long time-lapse experiments. These novel results establish a new low-cost and portable platform which can be used for spatially and temporally resolved experiments in diverse environments with a wide variety of microbial species. IMPORTANCEMicrobes live in diverse environments, and occupy biological roles across many timescales. Investigating the full scope of microbial activity requires imaging systems appropriate to each context. Though optical microscopy is powerful, the use of light, lenses, and other hardware limits where it can be applied. At the same time, existing non-optical imaging methods are frequently destructive to samples and require extensive equipment. In this paper we present a non-optical imaging system that is small, cheap, requires no sample labeling, and is compatible with a variety of microbial species. Our system uses semiconductor chips to measure the inherent material properties of a sample with spatial sensitivity, producing images of microbes contrasted against their environment and each other. Our technique captures label-free, micrometer-resolution images with a pocket-sized device, enabling microbiological imaging experiments in new environments with new species.

microbiology↗

bioRxiv2021

Super-Resolution Electrochemical Impedance Imaging with a 100 x 100 CMOS Sensor Array

This paper presents a 100 x 100 super-resolution integrated sensor array for microscale electrochemical impedance spectroscopy (EIS) imaging. The system is implemented in 180 nm CMOS with 10 m x 10 m pixels. Rather than treating each electrode independently, the sensor is designed to measure the mutual capacitance between programmable sets of pixels. Multiple spatially-resolved measurements can then be computationally combined to produce super-resolution impedance images. Experimental measurements of sub-cellular permittivity distributions within single algae cells demonstrate the potential of this new approach.

bioengineering↗

bioRxiv2021

CMOS electrochemical imaging arrays for thedetection and classification of microorganisms

Microorganisms account for most of the biodiversity on earth. Yet while there are increasingly powerful tools for studying microbial genetic diversity, there are fewer tools for studying microorganisms in their natural environments. In this paper, we present recent advances in CMOS electrochemical imaging arrays for detecting and classifying microorganisms. These microscale sensing platforms can provide non-optical measurements of cell geometries, behaviors, and metabolic markers. We review integrated electronic sensors appropriate for monitoring microbial growth, and present measurements of single-celled algae using a CMOS sensor array with thousands of active pixels. Integrated electrochemical imaging can contribute to improved medical diagnostics and environmental monitoring, as well as discoveries of new microbial populations.

bioengineering↗

bioRxiv2019

Encoding information in synthetic metabolomes

Biomolecular information systems offer numerous potential advantages over conventional semiconductor technologies. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic metabolomes. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which are decoded with accuracy exceeding 98-99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into the benefits and limitations of postgenomic chemical information systems.

synthetic biology↗