High-throughput ultrastructure screening using electron microscopy and fluorescent barcoding

Mitochondria in biology and medicine - 2023

Mitochondria are an indispensable part of the cell that plays a crucial role in regulating various signaling pathways, energy metabolism, cell differentiation, proliferation, and cell death. Since mitochondria have their own genetic material, they differ from their nuclear counterparts, and dysregulation is responsible for a broad spectrum of diseases. Mitochondrial dysfunction is associated with several disorders, including neuro-muscular disorders, cancer, and premature aging, among others. The intricacy of the field is due to the cross-talk between nuclear and mitochondrial genes, which has also improved our knowledge of mitochondrial functions and their pathogenesis. Therefore, interdisciplinary research and communication are crucial for mitochondrial biology and medicine due to the challenges they pose for diagnosis and treatment. The ninth annual conference of the Society for Mitochondria Research and Medicine (SMRM)- India, titled "Mitochondria in Biology and Medicine" was organized at the Centre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, India, on June 21-23, 2023. The latest advancements in the field of mitochondrial biology and medicine were discussed at the conference. In this article, we summarize the entire event for the benefit of researchers working in the field of mitochondrial biology and medicine.

Correlative Imaging to Detect Rare HIV Reservoirs and Associated Damage in Tissues

Correlative light-electron microscopy (CLEM) has evolved in the last decades, especially after significant developments in sample preparation, imaging acquisition, software, spatial resolution, and equipment, including confocal, live-cell, super-resolution, and electron microscopy (scanning, transmission, focused ion beam, and cryo-electron microscopy). However, the recent evolution of different laser-related techniques, such as mass spectrometry imaging (MSI) and laser capture microdissection, could further expand spatial imaging capabilities into high-resolution OMIC approaches such as proteomic, lipidomics, small molecule, and drug discovery. Here, we will describe a protocol to integrate the detection of rare viral reservoirs with imaging mass spectrometry.

Mind the gap: Methods to study membrane contact sites

Organelles are dynamic entities whose functions are essential for the optimum functioning of cells. It is now known that the juxtaposition of organellar membranes is essential for the exchange of metabolites and their communication. These functional apposition sites are termed membrane contact sites. Dynamic membrane contact sites between various sub-cellular structures such as mitochondria, endoplasmic reticulum, peroxisomes, Golgi apparatus, lysosomes, lipid droplets, plasma membrane, endosomes, etc. have been reported in various model systems. The burgeoning area of research on membrane contact sites has witnessed several manuscripts in recent years that identified the contact sites and components involved. Several methods have been developed to identify, measure and analyze the membrane contact sites. In this manuscript, we aim to discuss important methods developed to date that are used to study membrane contact sites.

Screening hydrogels for antifibrotic properties by implanting cellularly barcoded alginates in mice and a non-human primate

Screening implantable biomaterials for antifibrotic properties is constrained by the need for in vivo testing. Here we show that the throughput of in vivo screening can be increased by cellularly barcoding a chemically modified combinatorial library of hydrogel formulations. The method involves the implantation of a mixture of alginate formulations, each barcoded with human umbilical vein endothelial cells from different donors, and the association of the identity and performance of each formulation by genotyping single nucleotide polymorphisms of the cells via next-generation sequencing. We used the method to screen 20 alginate formulations in a single mouse and 100 alginate formulations in a single non-human primate, and identified three lead hydrogel formulations with antifibrotic properties. Encapsulating human islets with one of the formulations led to long-term glycaemic control in a mouse model of diabetes, and coating medical-grade catheters with the other two formulations prevented fibrotic overgrowth. High-throughput screening of barcoded biomaterials in vivo may help identify formulations that enhance the long-term performance of medical devices and of biomaterial-encapsulated therapeutic cells.

Insights Into the Biogenesis and Emerging Functions of Lipid Droplets From Unbiased Molecular Profiling Approaches

Lipid droplets (LDs) are spherical, single sheet phospholipid-bound organelles that store neutral lipids in all eukaryotes and some prokaryotes. Initially conceived as relatively inert depots for energy and lipid precursors, these highly dynamic structures play active roles in homeostatic functions beyond metabolism, such as proteostasis and protein turnover, innate immunity and defense. A major share of the knowledge behind this paradigm shift has been enabled by the use of systematic molecular profiling approaches, capable of revealing and describing these non-intuitive systems-level relationships. Here, we discuss these advances and some of the challenges they entail, and highlight standing questions in the field.

Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research

In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.

Image-Based Analysis Revealing the Molecular Mechanism of Peroxisome Dynamics in Plants

Peroxisomes are present in eukaryotic cells and have essential roles in various biological processes. Plant peroxisomes proliferate by de novo biosynthesis or division of pre-existing peroxisomes, degrade, or replace metabolic enzymes, in response to developmental stages, environmental changes, or external stimuli. Defects of peroxisome functions and biogenesis alter a variety of biological processes and cause aberrant plant growth. Traditionally, peroxisomal function-based screening has been employed to isolate Arabidopsis thaliana mutants that are defective in peroxisomal metabolism, such as lipid degradation and photorespiration. These analyses have revealed that the number, subcellular localization, and activity of peroxisomes are closely related to their efficient function, and the molecular mechanisms underlying peroxisome dynamics including organelle biogenesis, protein transport, and organelle interactions must be understood. Various approaches have been adopted to identify factors involved in peroxisome dynamics. With the development of imaging techniques and fluorescent proteins, peroxisome research has been accelerated. Image-based analyses provide intriguing results concerning the movement, morphology, and number of peroxisomes that were hard to obtain by other approaches. This review addresses image-based analysis of peroxisome dynamics in plants, especially A. thaliana and Marchantia polymorpha.

Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining

To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input. In this study, A549, HEK293, and NCI-H1975 cells were cultured, each of which have different molecular shapes and levels of drug responsiveness to doxorubicin (DOX). The microscopic images of these cells following exposure to various concentrations of DOX were trained with the measured value of cell viability using a colorimetric cell proliferation assay. Convolutional neural network (CNN) models for the study cells were constructed using augmented image data; the predicted cell viability using CNN models was compared to the cell viability measured by colorimetric assay. The linear relationship coefficient (r²) between measured and predicted cell viability was determined as 0.94–0.95 for the three cell types. In addition, the measured and predicted IC50 values were not statistically different. When drug responsiveness was estimated using allogenic models that were trained with a different cell type, the correlation coefficient decreased to 0.004085–0.8643. Our models could be applied to label-free cells to conduct rapid and large-scale tests while minimizing cost and labor, such as high-throughput screening for drug responsiveness.

Peroxisome function relies on organelle-associated mRNA translation

Crucial metabolic functions of peroxisomes rely on a variety of peroxisomal membrane proteins (PMPs). While mRNA transcripts of PMPs were shown to be colocalized with peroxisomes, the process by which PMPs efficiently couple translation with targeting to the peroxisomal membrane remained elusive. Here, we combine quantitative electron microscopy with proximity-specific ribosome profiling and reveal that translation of specific PMPs occurs on the surface of peroxisomes in the yeast Saccharomyces cerevisiae. This places peroxisomes alongside chloroplasts, mitochondria, and the endoplasmic reticulum as organelles that use localized translation for ensuring correct insertion of hydrophobic proteins into their membranes. Moreover, the correct targeting of these transcripts to peroxisomes is crucial for peroxisomal and cellular function, emphasizing the importance of localized translation for cellular physiology.

Show your true color: Mammalian cell surface staining for tracking cellular identity in multiplexing and beyond

Fluorescence microscopy revolutionized cell biology and changed requirements for dyes towards higher brightness, novel capacities, and specific targets. With the need for multiplexing assays in high-throughput methodologies, surface staining gained particular interest because it allows rapid application of exogenous stains to track cellular identity in mixed populations. Indeed, the last decade has enriched the toolbox of general lipid stains, fluorescent lipid analogues, sugar-binding lectins, and protein-specific antibodies enabling the first rationally designed plasma membrane-specific dyes. Still, multiple challenges exist, and the unique properties of each dye must be considered when selecting a staining approach for a specific application. Recent advances are also promising that future dyes will provide ultimate brightness and photostability in diverse colors and reduced sizes for high-resolution imaging.

Microscopes, tools, probes, and protocols: A guide in the route of correlative microscopy for biomedical investigation

In the last decades, the advancements of microscopes technology, together with the development of new imaging approaches, are trying to address some biological questions that have been unresolved in the past: the need to combine in the same analysis temporal, functional and morphological information on the biological sample has become pressing. For this reason, the use of correlative microscopy, in which two or more imaging techniques are combined in the same analysis, is getting increasingly widespread. In fact, correlative microscopy can overcome limitations of a single imaging method, giving access to a larger amount of information from the same specimen. However, correlative microscopy can be challenging, and appropriate protocols for sample preparation and imaging methods must be selected. Here we review the state of the art of correlating electron microscopy with different imaging methods, focusing on sample preparation, tools, and labeling methods, with the aim to provide a comprehensive guide for those scientists who are approaching the field of correlative methods.

The Fast and the Furious: Golgi Contact Sites

Contact sites are areas of close apposition between two membranes that coordinate nonvesicular communication between organelles. Such interactions serve a wide range of cellular functions from regulating metabolic pathways to executing stress responses and coordinating organelle inheritance. The past decade has seen a dramatic increase in information on certain contact sites, mostly those involving the endoplasmic reticulum. However, despite its central role in the secretory pathway, the Golgi apparatus and its contact sites remain largely unexplored. In this review, we discuss the current knowledge of Golgi contact sites and share our thoughts as to why Golgi contact sites are understudied. We also highlight what exciting future directions may exist in this emerging field.

Fluorescent and Electron-Dense Green Color Emitting Nanodiamonds for Single-Cell Correlative Microscopy

Correlative light and electron microscopy (CLEM) is revolutionizing how cell samples are studied. CLEM provides a combination of the molecular and ultrastructural information about a cell. For the execution of CLEM experiments, multimodal fiducial landmarks are applied to precisely overlay light and electron microscopy images. Currently applied fiducials such as quantum dots and organic dye-labeled nanoparticles can be irreversibly quenched by electron beam exposure during electron microscopy. Generally, the sample is therefore investigated with a light microscope first and later with an electron microscope. A versatile fiducial landmark should offer to switch back from electron microscopy to light microscopy while preserving its fluorescent properties. Here, we evaluated green fluorescent and electron dense nanodiamonds for the execution of CLEM experiments and precisely correlated light microscopy and electron microscopy images. We demonstrated that green color emitting fluorescent nanodiamonds withstand electron beam exposure, harsh chemical treatments, heavy metal straining, and, importantly, their fluorescent properties remained intact for light microscopy.

Photonic-chip assisted correlative light and electron microscopy

Correlative light and electron microscopy (CLEM) unifies the versatility of light microscopy (LM) with the high resolution of electron microscopy (EM), allowing one to zoom into the complex organization of cells. Here, we introduce photonic chip assisted CLEM, enabling multi-modal total internal reflection fluorescence (TIRF) microscopy over large field of view and high precision localization of the target area of interest within EM. The photonic chips are used as a substrate to hold, to illuminate and to provide landmarking of the sample through specially designed grid-like numbering systems. Using this approach, we demonstrate its applicability for tracking the area of interest, imaging the three-dimensional (3D) structural organization of nano-sized morphological features on liver sinusoidal endothelial cells such as fenestrations (trans-cytoplasmic nanopores), and correlating specific endo-lysosomal compartments with its cargo protein upon endocytosis.

Tinguely et al. develop a photonic chip-based correlative light-electron microscopy system to generate co-registered multi-modal total internal reflection fluorescence microscopy (TIRF) and electron microscopy (EM) images of biological samples at nanometer scale.

OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages

Various pre-trained deep learning models for the segmentation of bioimages have been made available as developer-to-end-user solutions. They are optimized for ease of use and usually require neither knowledge of machine learning nor coding skills. However, individually testing these tools is tedious and success is uncertain. Here, we present the Open Segmentation Framework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts' knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and postprocessing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and postprocessing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data. We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows. Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little; the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease.

Natural display of nuclear-encoded RNA on the cell surface and its impact on cell interaction

BACKGROUND

Compared to proteins, glycans, and lipids, much less is known about RNAs on the cell surface. We develop a series of technologies to test for any nuclear-encoded RNAs that are stably attached to the cell surface and exposed to the extracellular space, hereafter called membrane-associated extracellular RNAs (maxRNAs).

RESULTS

We develop a technique called Surface-seq to selectively sequence maxRNAs and validate two Surface-seq identified maxRNAs by RNA fluorescence in situ hybridization. To test for cell-type specificity of maxRNA, we use antisense oligos to hybridize to single-stranded transcripts exposed on the surface of human peripheral blood mononuclear cells (PBMCs). Combining this strategy with imaging flow cytometry, single-cell RNA sequencing, and maxRNA sequencing, we identify monocytes as the major type of maxRNA+ PBMCs and prioritize 11 candidate maxRNAs for functional tests. Extracellular application of antisense oligos of FNDC3B and CTSS transcripts inhibits monocyte adhesion to vascular endothelial cells.

CONCLUSIONS

Collectively, these data highlight maxRNAs as functional components of the cell surface, suggesting an expanded role for RNA in cell-cell and cell-environment interactions.