The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceutical cell lines based on their intrinsic molecular contrast. Employing simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy with fluorescence lifetime imaging microscopy (FLIM), we characterized Chinese hamster ovary (CHO) cell lines at early passages (0-2). A machine learning (ML)-assisted analysis pipeline leveraged high-dimensional information to classify single cells into their respective lines. Remarkably, the monoclonal cell line classifiers achieved balanced accuracies exceeding 96.8% as early as passage 2. Correlation features and FLIM modality played pivotal roles in early classification. This integrated optical bioimaging and machine learning approach presents a promising solution to expedite cell line selection process while ensuring identification of high-performing biopharmaceutical cell lines. The techniques have potential for broader single-cell characterization applications in stem cell research, immunology, cancer biology and beyond.

Hierarchic self-assembly is the main mechanism used to create diverse structures using soft materials. This is a case for both synthetic materials and biomolecular systems, as exemplified by the non-covalent organization of lipids into membranes. In nature, lipids often assemble into single bilayers, but other nanostructures are encountered, such as bilayer stacks and tubular and vesicular aggregates. Synthetic block copolymers can be engineered to recapitulate many of the structures, forms, and functions of lipid systems. When block copolymers are amphiphilic, they can be inserted or co-assembled into hybrid membranes that exhibit synergistic structural, permeability, and mechanical properties. One example is the emergence of lateral phase separation akin to the raft formation in biomembranes. When higher-order structures, such as hybrid membranes, are formed, this lateral phase separation can be correlated across membranes in the stack. This chapter outlines a set of important methods, such as X-ray Scattering, Atomic Force Microscopy, and Cryo-Electron Microscopy, that are relevant to characterizing and evaluating lateral and correlated phase separation in hybrid membranes at the nano and mesoscales. Understanding the phase behavior of polymer-lipid hybrid materials could lead to innovative advancements in biomimetic membrane separation systems.

The COVID-19 pandemic triggered the resurgence of synthetic RNA vaccine platforms allowing rapid, scalable, low-cost manufacturing, and safe administration of therapeutic vaccines. Self-amplifying mRNA (SAM), which self-replicates upon delivery into the cellular cytoplasm, leads to a strong and sustained immune response. Such mRNAs are encapsulated within lipid nanoparticles (LNPs) that act as a vehicle for delivery to the cell cytoplasm. A better understanding of LNP-mediated SAM uptake and release mechanisms in different types of cells is critical for designing effective vaccines. Here, we investigated the cellular uptake of a SAM-LNP formulation and subsequent intracellular expression of SAM in baby hamster kidney (BHK-21) cells using hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy and multiphoton-excited fluorescence lifetime imaging microscopy (FLIM). Cell classification pipelines based on HS-CARS and FLIM features were developed to obtain insights on spectral and metabolic changes associated with SAM-LNPs uptake. We observed elevated lipid intensities with the HS-CARS modality in cells treated with LNPs versus PBS-treated cells, and simultaneous fluorescence images revealed SAM expression inside BHK-21 cell nuclei and cytoplasm within 5 h of treatment. In a separate experiment, we observed a strong correlation between the SAM expression and mean fluorescence lifetime of the bound NAD(P)H population. This work demonstrates the ability and significance of multimodal optical imaging techniques to assess the cellular uptake of SAM-LNPs and the subsequent changes occurring in the cellular microenvironment following the vaccine expression.

Label-free multimodal optical bioimaging allows non-perturbative profiling of biological samples based on their intrinsic optical molecular properties. In this study, we utilized SLAM and FLIM microscopy to identify CHO cell lines with favorable process performance for the production of therapeutic monoclonal antibodies and proteins. Here, a single-cell analysis pipeline was developed to quantitatively characterize CHO cell lines based on their phenotypes. To perceive the rich information in the multi-modal bioimages, a custom-built multi-task deep neural network was built, which can extract features from different aspects of the optical and molecular properties of the sample. This work demonstrated the potential of ML-assisted multi-modal optical imaging in the identification of cell lines with desirable characteristics for biopharmaceutical production at earlier time points.

Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene. Typically, this is performed using consumer cameras because of their low cost, human interpretability, and high resolution. Yet, cameras and the BFS algorithms that process their data have common failure modes due to lighting changes, highly reflective surfaces, and occlusion. One solution is to incorporate an additional sensor modality that provides robustness to such failure modes. In this paper, we explore the ability of a cost-effective radar system to augment the popular Robust PCA technique for BFS. We apply the emerging technique of algorithm unrolling to yield real-time computation, feedforward inference, and strong generalization in comparison with traditional deep learning methods. We benchmark on the RaDICaL dataset to demonstrate both quantitative improvements of incorporating radar data and qualitative improvements that confirm robustness to common failure modes of image-based methods.

Traffic cameras are becoming a popular form of surveillance in traffic bodies across the country. With recent advances in computer vision and deep learning, there is a great opportunity to leverage these images to provide real-time traffic estimates and other public services. In this report, we detail some of the challenges, current interests of the research community, and our early efforts in using traffic cameras for vehicle detection and traffic estimation. We also discuss a benchmark traffic dataset that we are assembling and plan to release to the research community to motivate further work in this area.

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban traffic body. Our dataset provides over 4 million still images across 2.5 months and one hundred web cameras in suburban Lake County, IL. We divide the cameras into two distinct communities described by directed graphs and count vehicles to track traffic statistics. Our goal is to give researchers a benchmark dataset for exploring the capabilities of inexpensive and non-invasive sensors like web cameras to understand complex traffic bodies in communities of any size. We present benchmarking tasks and baseline results for one such task to guide how future work may use our dataset.