Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing

Multiplex, Quantitative, High-Resolution Imaging of Protein:Protein Complexes via Hybridization Chain Reaction

Signal amplification based on the mechanism of hybridization chain reaction (HCR) facilitates spatial exploration of gene regulatory networks by enabling multiplex, quantitative, high-resolution imaging of RNA and protein targets. Here, we extend these capabilities to the imaging of protein:protein complexes, using proximity-dependent cooperative probes to conditionally generate a single amplified signal if and only if two target proteins are colocalized within the sample. HCR probes and amplifiers combine to provide automatic background suppression throughout the protocol, ensuring that even if reagents bind nonspecifically in the sample, they will not generate amplified background. We demonstrate protein:protein imaging with a high signal-to-background ratio in human cells, mouse proT cells, and highly autofluorescent formalin-fixed paraffin-embedded (FFPE) human breast tissue sections. Further, we demonstrate multiplex imaging of three different protein:protein complexes simultaneously and validate that HCR enables accurate and precise relative quantitation of protein:protein complexes with subcellular resolution in an anatomical context. Moreover, we establish a unified framework for simultaneous multiplex, quantitative, high-resolution imaging of RNA, protein, and protein:protein targets, with one-step, isothermal, enzyme-free HCR signal amplification performed for all target classes simultaneously.

HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples

Signal amplification based on the mechanism of hybridization chain reaction (HCR) provides a unified framework for multiplex, quantitative, high-resolution imaging of RNA and protein targets in highly autofluorescent samples. With conventional bandpass imaging, multiplexing is typically limited to four or five targets owing to the difficulty in separating signals generated by fluorophores with overlapping spectra. Spectral imaging has offered the conceptual promise of higher levels of multiplexing, but it has been challenging to realize this potential in highly autofluorescent samples, including whole-mount vertebrate embryos. Here, we demonstrate robust HCR spectral imaging with linear unmixing, enabling simultaneous imaging of ten RNA and/or protein targets in whole-mount zebrafish embryos and mouse brain sections. Further, we demonstrate that the amplified and unmixed signal in each of the ten channels is quantitative, enabling accurate and precise relative quantitation of RNA and/or protein targets with subcellular resolution, and RNA absolute quantitation with single-molecule resolution, in the anatomical context of highly autofluorescent samples.

Lanthanide-Complex-Enhanced Bioorthogonal Branched DNA Amplification

Fluorescence in situ hybridization (FISH) is a widely used technique for detecting intracellular nucleic acids. However, its effectiveness in detecting low-copy nucleic acids is limited due to its low fluorescence intensity and background autofluorescence. To address these challenges, we present here an approach of lanthanide-complex-enhanced bioorthogonal-branched DNA amplification (LEBODA) with high sensitivity for in situ nuclear acid detection in single cells. The approach capitalizes on two levels of signal amplification. First, it utilizes click chemistry to directly link a substantial number of bridge probes to target-recognizing probes, providing an initial boost in signal intensity. Second, it incorporates high-density lanthanide complexes into each bridge probe, enabling secondary amplifications. Compared to the traditional "double Z" probes used in the RNAscope method, LEBODA exhibits 4 times the single enhancement for RNA detection signal with the click chemistry approach. Using SARS-CoV-2 pseudovirus-infected HeLa cells, we demonstrate the superiority in the detection of viral-infected cells in rare populations as low as 20% infectious rate. More encouragingly, the LEBODA approach can be adapted for DNA-FISH and single-molecule RNA-FISH, as well as other hybridization-based signal amplification methods. This adaptability broadens the potential applications of LEBODA in the sensitive detection of biomolecules, indicating promising prospects for future research and practical use.

Hybridization Chain Reaction RNA Whole-Mount Fluorescence In situ Hybridization of Chemosensory Genes in Mosquito Olfactory Appendages

Mosquitoes are effective vectors of deadly diseases and can navigate their chemical environment using chemosensory receptors expressed in their olfactory appendages. Understanding how chemosensory receptors are spatially organized in the peripheral olfactory appendages can offer insights into how odor is encoded in the mosquito olfactory system and inform new ways to combat the spread of mosquito-borne diseases. The emergence of third-generation hybridization chain reaction RNA whole-mount fluorescence in situ hybridization (HCR RNA WM-FISH) allows for spatial mapping and simultaneous expression profiling of multiple chemosensory genes. Here, we describe a stepwise approach for performing HCR RNA WM-FISH on the Anopheles mosquito antenna and maxillary palp. We investigated the sensitivity of this technique by examining the expression profile of ionotropic olfactory receptors. We asked if the HCR WM-FISH technique described was suitable for multiplexed studies by tethering RNA probes to three spectrally distinct fluorophores. Results provided evidence that HCR RNA WM-FISH is robustly sensitive to simultaneously detect multiple chemosensory genes in the antenna and maxillary palp olfactory appendages. Further investigations attest to the suitability of HCR WM-FISH for co-expression profiling of double and triple RNA targets. This technique, when applied with modifications, could be adaptable to localize genes of interest in the olfactory tissues of other insect species or in other appendages.

Enablers and challenges of spatial omics, a melting pot of technologies

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

Gm527 deficiency in dentate gyrus improves memory through upregulating dopamine D1 receptor pathway

AIMS

Dopamine D1 receptor (D1R) hypofunction is associated with negative and cognitive symptoms in schizophrenia; therefore, the mechanism of D1R function modulation needs further investigation. Gm527 is the rodent homologous of the schizophrenia-related gene C14orf28, encoding a predicated D1R-interacting protein. However, the role of Gm527-D1R interaction in schizophrenia needs to be clarified.

METHODS

Gm527-floxed mice were generated and crossed with D1-Cre mice (D1:Gm527-/-) to knockout Gm527 in D1R-positive neurons. Then behavioral tests were performed to explore the schizophrenia-related phenotypes. Immunofluorescence, fluorescence in situ hybridization, electrophysiological recording, quantitative real-time PCR, and western blotting were conducted to investigate the mechanisms.

RESULTS

Working memory, long-term memories, and adult neurogenesis in the DG were enhanced in D1:Gm527-/- mice. LTP was also increased in the DG in D1:Gm527-/- mice, resulting from the Gm527 knockout-induced D1R expression enhancement on the plasma membrane and subsequently cAMP signaling and NMDA receptor pathways activation. The requirement of Gm527 knockout in the DG was confirmed by reversing Gm527 expression or knockdown Gm527 in the DG D1R-positive neurons through AAV-CAG-FLEX-Gm527-GFP or AAV-CMV-FLEX-EGFP-Gm527-RNAi injection.

CONCLUSIONS

The DG Gm527 knockout induces D1R hyperfunction in improving schizophrenia cognitive symptoms.

Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling

In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.

Following the Birth, Life, and Death of mRNAs in Single Cells

Recent advances in single-molecule imaging of mRNAs in fixed and living cells have enabled the lives of mRNAs to be studied with unprecedented spatial and temporal detail. These approaches have moved beyond simply being able to observe specific events and have begun to allow an understanding of how regulation is coupled between steps in the mRNA life cycle. Additionally, these methodologies are now being applied in multicellular systems and animals to provide more nuanced insights into the physiological regulation of RNA metabolism.

Quantitative whole-tissue 3D imaging reveals bacteria in close association with mouse jejunum mucosa

Because the small intestine (SI) epithelium lacks a thick protective mucus layer, microbes that colonize the thin SI mucosa may exert a substantial effect on the host. For example, bacterial colonization of the human SI may contribute to environmental enteropathy dysfunction (EED) in malnourished children. Thus far, potential bacterial colonization of the mucosal surface of the SI has only been documented in disease states, suggesting mucosal colonization is rare, likely requiring multiple perturbations. Furthermore, conclusive proof of bacterial colonization of the SI mucosal surface is challenging, and the three-dimensional (3D) spatial structure of mucosal colonies remains unknown. Here, we tested whether we could induce dense bacterial association with jejunum mucosa by subjecting mice to a combination of malnutrition and oral co-gavage with a bacterial cocktail (E. coli and Bacteroides spp.) known to induce EED. To visualize these events, we optimized our previously developed whole-tissue 3D imaging tools with third-generation hybridization chain reaction (HCR v3.0) probes. Only in mice that were malnourished and gavaged with the bacterial cocktail did we detect dense bacterial clusters surrounding intestinal villi suggestive of colonization. Furthermore, in these mice we detected villus loss, which may represent one possible consequence that bacterial colonization of the SI mucosa has on the host. Our results suggest that dense bacterial colonization of jejunum mucosa is possible in the presence of multiple perturbations and that whole-tissue 3D imaging tools can enable the study of these rare events.

Causal Genetic Loci for a Motivated Behavior Spectrum Harbor Psychiatric Risk Genes

Behavioral diversity is critical for population fitness. Individual differences in risk-taking are observed across species, but underlying genetic mechanisms and conservation are largely unknown. We examined dark avoidance in larval zebrafish, a motivated behavior reflecting an approach-avoidance conflict. Brain-wide calcium imaging revealed significant neural activity differences between approach-inclined versus avoidance-inclined individuals. We used a population of ∼6,000 to perform the first genome-wide association study (GWAS) in zebrafish, which identified 34 genomic regions harboring many genes that are involved in synaptic transmission and human psychiatric diseases. We used CRISPR to study several causal genes: serotonin receptor-1b ( htr1b ), nitric oxide synthase-1 ( nos1 ), and stress-induced phosphoprotein-1 ( stip1 ). We further identified 52 conserved elements containing 66 GWAS significant variants. One encoded an exonic regulatory element that influenced tissue-specific nos1 expression. Together, these findings reveal new genetic loci and establish a powerful, scalable animal system to probe mechanisms underlying motivation, a critical dimension of psychiatric diseases.

HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples

Signal amplification based on the mechanism of hybridization chain reaction (HCR) provides a unified framework for multiplex, quantitative, high-resolution imaging of RNA and protein targets in highly autofluorescent samples. With conventional bandpass imaging, multiplexing is typically limited to four or five targets due to the difficulty in separating signals generated by fluorophores with overlapping spectra. Spectral imaging has offered the conceptual promise of higher levels of multiplexing, but it has been challenging to realize this potential in highly autofluorescent samples including whole-mount vertebrate embryos. Here, we demonstrate robust HCR spectral imaging with linear unmixing, enabling simultaneous imaging of 10 RNA and/or protein targets in whole-mount zebrafish embryos and mouse brain sections. Further, we demonstrate that the amplified and unmixed signal in each of 10 channels is quantitative, enabling accurate and precise relative quantitation of RNA and/or protein targets with subcellular resolution, and RNA absolute quantitation with single-molecule resolution, in the anatomical context of highly autofluorescent samples.

SUMMARY

Spectral imaging with signal amplification based on the mechanism of hybridization chain reaction enables robust 10-plex, quantitative, high-resolution imaging of RNA and protein targets in whole-mount vertebrate embryos and brain sections.

Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

The dawn of spatial omics

Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

New discoveries in the field of metabolism by applying single-cell and spatial omics

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex

Rodent studies have demonstrated that synaptic dynamics from excitatory to inhibitory neuron types are often dependent on the target cell type. However, these target cell-specific properties have not been well investigated in human cortex, where there are major technical challenges in reliably obtaining healthy tissue, conducting multiple patch-clamp recordings on inhibitory cell types, and identifying those cell types. Here, we take advantage of newly developed methods for human neurosurgical tissue analysis with multiple patch-clamp recordings, post-hoc fluorescent in situ hybridization (FISH), machine learning-based cell type classification and prospective GABAergic AAV-based labeling to investigate synaptic properties between pyramidal neurons and PVALB- vs. SST-positive interneurons. We find that there are robust molecular differences in synapse-associated genes between these neuron types, and that individual presynaptic pyramidal neurons evoke postsynaptic responses with heterogeneous synaptic dynamics in different postsynaptic cell types. Using molecular identification with FISH and classifiers based on transcriptomically identified PVALB neurons analyzed by Patch-seq, we find that PVALB neurons typically show depressing synaptic characteristics, whereas other interneuron types including SST-positive neurons show facilitating characteristics. Together, these data support the existence of target cell-specific synaptic properties in human cortex that are similar to rodent, thereby indicating evolutionary conservation of local circuit connectivity motifs from excitatory to inhibitory neurons and their synaptic dynamics.

TSA-PACT: a method for tissue clearing and immunofluorescence staining on zebrafish brain with improved sensitivity, specificity and stability

For comprehensive studies of the brain structure and function, fluorescence imaging of the whole brain is essential. It requires large-scale volumetric imaging in cellular or molecular resolution, which could be quite challenging. Recent advances in tissue clearing technology (e.g. CLARITY, PACT) provide new solutions by homogenizing the refractive index of the samples to create transparency. However, it has been difficult to acquire high quality results through immunofluorescence (IF) staining on the cleared samples. To address this issue, we developed TSA-PACT, a method combining tyramide signal amplification (TSA) and PACT, to transform samples into hydrogel polymerization frameworks with covalent fluorescent biomarkers assembled. We show that TSA-PACT is able to reduce the opacity of the zebrafish brain by more than 90% with well-preserved structure. Compared to traditional method, TSA-PACT achieves approximately tenfold signal amplification and twofold improvement in signal-to-noise ratio (SNR). Moreover, both the structure and the fluorescent signal persist for at least 16 months with excellent signal retention ratio. Overall, this method improves immunofluorescence signal sensitivity, specificity and stability in the whole brain of juvenile and adult zebrafish, which is applicable for fine structural analysis, neural circuit mapping and three-dimensional cell counting.

Advances in spatial transcriptomics and related data analysis strategies

Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Tissue clearing and immunostaining to visualize the spatial organization of vasculature and tumor cells in mouse liver

The liver has a complex and hierarchical segmental organization of arteries, portal veins, hepatic veins and lymphatic vessels. In-depth imaging of liver vasculature and malignancies could improve knowledge on tumor micro-environment, local tumor growth, invasion, as well as metastasis. Non-invasive imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission transmission (PET) are routine for clinical imaging, but show inadequate resolution at cellular and subcellular level. In recent years, tissue clearing – a technique rendering tissues optically transparent allowing enhanced microscopy imaging – has made great advances. While mainly used in the neurobiology field, recently more studies have used clearing techniques for imaging other organ systems as well as tumor tissues. In this study, our aim was to develop a reproducible tissue clearing and immunostaining model for visualizing intrahepatic blood microvasculature and tumor cells in murine colorectal liver metastases. CLARITY and 3DISCO/iDISCO+ are two established clearing methods that have been shown to be compatible with immunolabelling, most often in neurobiology research. In this study, CLARITY unfortunately resulted in damaged tissue integrity of the murine liver lobes and no specific immunostaining. Using the 3DISCO/iDISCO+ method, liver samples were successfully rendered optically transparent. After which, successful immunostaining of the intrahepatic microvasculature using panendothelial cell antigen MECA-32 and colorectal cancer cells using epithelial cell adhesion molecule (EpCAM) was established. This approach for tumor micro-environment tissue clearing would be especially valuable for allowing visualization of spatial heterogeneity and complex interactions of tumor cells and their environment in future studies.

Hybridization Chain Reaction Lateral Flow Assays for Amplified Instrument-Free At-Home SARS-CoV-2 Testing

The lateral flow assay format enables rapid, instrument-free, at-home testing for SARS-CoV-2. Due to the absence of signal amplification, this simplicity comes at a cost in sensitivity. Here, we enhance sensitivity by developing an amplified lateral flow assay that incorporates isothermal, enzyme-free signal amplification based on the mechanism of hybridization chain reaction (HCR). The simplicity of the user experience is maintained using a disposable 3-channel lateral flow device to automatically deliver reagents to the test region in three successive stages without user interaction. To perform a test, the user loads the sample, closes the device, and reads the result by eye after 60 min. Detecting gamma-irradiated SARS-CoV-2 virions in a mixture of saliva and extraction buffer, the current amplified HCR lateral flow assay achieves a limit of detection of 200 copies/μL using available antibodies to target the SARS-CoV-2 nucleocapsid protein. By comparison, five commercial unamplified lateral flow assays that use proprietary antibodies exhibit limits of detection of 500 copies/μL, 1000 copies/μL, 2000 copies/μL, 2000 copies/μL, and 20,000 copies/μL. By swapping out antibody probes to target different pathogens, amplified HCR lateral flow assays offer a platform for simple, rapid, and sensitive at-home testing for infectious diseases. As an alternative to viral protein detection, we further introduce an HCR lateral flow assay for viral RNA detection.

RS-FISH: precise, interactive, fast, and scalable FISH spot detection

Fluorescent in-situ hybridization (FISH)-based methods extract spatially resolved genetic and epigenetic information from biological samples by detecting fluorescent spots in microscopy images, an often challenging task. We present Radial Symmetry-FISH (RS-FISH), an accurate, fast, and user-friendly software for spot detection in two- and three-dimensional images. RS-FISH offers interactive parameter tuning and readily scales to large datasets and image volumes of cleared or expanded samples using distributed processing on workstations, clusters, or the cloud. RS-FISH maintains high detection accuracy and low localization error across a wide range of signal-to-noise ratios, a key feature for single-molecule FISH, spatial transcriptomics, or spatial genomics applications.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

The impact of single-cell genomics on the field of mycobacterial infection

Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.

Nanoscale fluorescence imaging of biological ultrastructure via molecular anchoring and physical expansion

Nanoscale imaging of biological samples can provide rich morphological and mechanistic information about biological functions and dysfunctions at the subcellular and molecular level. Expansion microscopy (ExM) is a recently developed nanoscale fluorescence imaging method that takes advantage of physical enlargement of biological samples. In ExM, preserved cells and tissues are embedded in a swellable hydrogel, to which the molecules and fluorescent tags in the samples are anchored. When the hydrogel swells several-fold, the effective resolution of the sample images can be improved accordingly via physical separation of the retained molecules and fluorescent tags. In this review, we focus on the early conception and development of ExM from a biochemical and materials perspective. We first examine the general workflow as well as the numerous variations of ExM developed to retain and visualize a broad range of biomolecules, such as proteins, nucleic acids, and membranous structures. We then describe a number of inherent challenges facing ExM, including those associated with expansion isotropy and labeling density, as well as the ongoing effort to address these limitations. Finally, we discuss the prospect and possibility of pushing the resolution and accuracy of ExM to the single-molecule scale and beyond.

Optimization of Whole Mount RNA Multiplexed in situ Hybridization Chain Reaction With Immunohistochemistry, Clearing and Imaging to Visualize Octopus Embryonic Neurogenesis

Gene expression analysis has been instrumental to understand the function of key factors during embryonic development of many species. Marker analysis is also used as a tool to investigate organ functioning and disease progression. As these processes happen in three dimensions, the development of technologies that enable detection of gene expression in the whole organ or embryo is essential. Here, we describe an optimized protocol of whole mount multiplexed RNA in situ hybridization chain reaction version 3.0 (HCR v3.0) in combination with immunohistochemistry (IHC), followed by fructose-glycerol clearing and light sheet fluorescence microscopy (LSFM) imaging on Octopus vulgaris embryos. We developed a code to automate probe design which can be applied for designing HCR v3.0 type probe pairs for fluorescent in situ mRNA visualization. As proof of concept, neuronal (Ov-elav) and glial (Ov-apolpp) markers were used for multiplexed HCR v3.0. Neural progenitor (Ov-ascl1) and precursor (Ov-neuroD) markers were combined with immunostaining for phosphorylated-histone H3, a marker for mitosis. After comparing several tissue clearing methods, fructose-glycerol clearing was found optimal in preserving the fluorescent signal of HCR v3.0. The expression that was observed in whole mount octopus embryos matched with the previous expression data gathered from paraffin-embedded transverse sections. Three-dimensional reconstruction revealed additional spatial organization that had not been discovered using two-dimensional methods.

Molecular profiling of enteric nervous system cell lineages

The enteric nervous system (ENS) is an extensive network of enteric neurons and glial cells that is intrinsic to the gut wall and regulates almost all aspects of intestinal physiology. While considerable advancement has been made in understanding the genetic programs regulating ENS development, there is limited understanding of the molecular pathways that control ENS function in adult stages. One of the limitations in advancing the molecular characterization of the adult ENS relates to technical difficulties in purifying healthy neurons and glia from adult intestinal tissues. To overcome this, we developed novel methods for performing transcriptomic analysis of enteric neurons and glia, which are based on the isolation of fluorescently labeled nuclei. Here we provide a step-by-step protocol for the labeling of adult mouse enteric neuronal nuclei using adeno-associated-virus-mediated gene transfer, isolation of the labeled nuclei by fluorimetric analysis, RNA purification and nuclear RNA sequencing. This protocol has also been adapted for the isolation of enteric neuron and glia nuclei from myenteric plexus preparations from adult zebrafish intestine. Finally, we describe a method for visualization and quantification of RNA in myenteric ganglia: Spatial Integration of Granular Nuclear Signals (SIGNS). By following this protocol, it takes ~3 d to generate RNA and create cDNA libraries for nuclear RNA sequencing and 4 d to carry out high-resolution RNA expression analysis on ENS tissues.

Recent advances in spatially resolved transcriptomics: challenges and opportunities

Single-cell RNA sequencing (scRNA-seq) has greatly advanced our understanding of cellular heterogeneity by profiling individual cell transcriptomes. However, cell dissociation from the tissue structure causes a loss of spatial information, which hinders the identification of intercellular communication networks and global transcriptional patterns present in the tissue architecture. To overcome this limitation, novel transcriptomic platforms that preserve spatial information have been actively developed. Significant achievements in imaging technologies have enabled in situ targeted transcriptomic profiling in single cells at singlemolecule resolution. In addition, technologies based on mRNA capture followed by sequencing have made possible profiling of the genome-wide transcriptome at the 55-100 μm resolution. Unfortunately, neither imaging-based technology nor capturebased method elucidates a complete picture of the spatial transcriptome in a tissue. Therefore, addressing specific biological questions requires balancing experimental throughput and spatial resolution, mandating the efforts to develop computational algorithms that are pivotal to circumvent technology-specific limitations. In this review, we focus on the current state-of-the-art spatially resolved transcriptomic technologies, describe their applications in a variety of biological domains, and explore recent discoveries demonstrating their enormous potential in biomedical research. We further highlight novel integrative computational methodologies with other data modalities that provide a framework to derive biological insight into heterogeneous and complex tissue organization. [BMB Reports 2022; 55(3): 113-124].

TISSUE CLEARING

Tissue clearing of gross anatomical samples was first described over a century ago and has only recently found widespread use in the field of microscopy. This renaissance has been driven by the application of modern knowledge of optical physics and chemical engineering to the development of robust and reproducible clearing techniques, the arrival of new microscopes that can image large samples at cellular resolution and computing infrastructure able to store and analyze large data volumes. Many biological relationships between structure and function require investigation in three dimensions and tissue clearing therefore has the potential to enable broad discoveries in the biological sciences. Unfortunately, the current literature is complex and could confuse researchers looking to begin a clearing project. The goal of this Primer is to outline a modular approach to tissue clearing that allows a novice researcher to develop a customized clearing pipeline tailored to their tissue of interest. Further, the Primer outlines the required imaging and computational infrastructure needed to perform tissue clearing at scale, gives an overview of current applications, discusses limitations and provides an outlook on future advances in the field.

The biogeography of infection revisited

Many microbial communities, including those involved in chronic human infections, are patterned at the micron scale. In this Review, we summarize recent work that has defined the spatial arrangement of microorganisms in infection and begun to demonstrate how changes in spatial patterning correlate with disease. Advances in microscopy have refined our understanding of microbial micron-scale biogeography in samples from humans. These findings then serve as a benchmark for studying the role of spatial patterning in preclinical models, which provide experimental versatility to investigate the interplay between biogeography and pathogenesis. Experimentation using preclinical models has begun to show how spatial patterning influences the interactions between cells, their ability to coexist, their virulence and their recalcitrance to treatment. Future work to study the role of biogeography in infection and the functional biogeography of microorganisms will further refine our understanding of the interplay of spatial patterning, pathogen virulence and disease outcomes.

Dense functional and molecular readout of a circuit hub in sensory cortex

Although single-cell transcriptomics of the neocortex has uncovered more than 300 putative cell types, whether this molecular classification predicts distinct functional roles is unclear. We combined two-photon calcium imaging with spatial transcriptomics to functionally and molecularly investigate cortical circuits. We characterized behavior-related responses across major neuronal subclasses in layers 2 or 3 of the primary somatosensory cortex as mice performed a tactile working memory task. We identified an excitatory intratelencephalic cell type, Baz1a, that exhibits high tactile feature selectivity. Baz1a neurons homeostatically maintain stimulus responsiveness during altered experience and show persistent enrichment of subsets of immediately early genes. Functional and anatomical connectivity reveals that Baz1a neurons residing in upper portions of layers 2 or 3 preferentially innervate somatostatin-expressing inhibitory neurons. This motif defines a circuit hub that orchestrates local sensory processing in superficial layers of the neocortex.

Hybridization chain reaction enables a unified approach to multiplexed, quantitative, high-resolution immunohistochemistry and in situ hybridization

RNA in situ hybridization based on the mechanism of the hybridization chain reaction (HCR) enables multiplexed, quantitative, high-resolution RNA imaging in highly autofluorescent samples, including whole-mount vertebrate embryos, thick brain slices and formalin-fixed paraffin-embedded tissue sections. Here, we extend the benefits of one-step, multiplexed, quantitative, isothermal, enzyme-free HCR signal amplification to immunohistochemistry, enabling accurate and precise protein relative quantitation with subcellular resolution in an anatomical context. Moreover, we provide a unified framework for simultaneous quantitative protein and RNA imaging with one-step HCR signal amplification performed for all target proteins and RNAs simultaneously.

Microtubule-based transport is essential to distribute RNA and nascent protein in skeletal muscle

While the importance of RNA localization in highly differentiated cells is well appreciated, basic principles of RNA localization in skeletal muscle remain poorly characterized. Here, we develop a method to detect and quantify single molecule RNA localization patterns in skeletal myofibers, and uncover a critical role for directed transport of RNPs in muscle. We find that RNAs localize and are translated along sarcomere Z-disks, dispersing tens of microns from progenitor nuclei, regardless of encoded protein function. We find that directed transport along the lattice-like microtubule network of myofibers becomes essential to achieve this localization pattern as muscle development progresses; disruption of this network leads to extreme accumulation of RNPs and nascent protein around myonuclei. Our observations suggest that global active RNP transport may be required to distribute RNAs in highly differentiated cells and reveal fundamental mechanisms of gene regulation, with consequences for myopathies caused by perturbations to RNPs or microtubules.

Deep Parallel Characterization of AAV Tropism and AAV-Mediated Transcriptional Changes via Single-Cell RNA Sequencing

Engineered variants of recombinant adeno-associated viruses (rAAVs) are being developed rapidly to meet the need for gene-therapy delivery vehicles with particular cell-type and tissue tropisms. While high-throughput AAV engineering and selection methods have generated numerous variants, subsequent tropism and response characterization have remained low throughput and lack resolution across the many relevant cell and tissue types. To fully leverage the output of these large screening paradigms across multiple targets, we have developed an experimental and computational single-cell RNA sequencing (scRNA-seq) pipeline for in vivo characterization of barcoded rAAV pools at high resolution. Using this platform, we have both corroborated previously reported viral tropisms and discovered unidentified AAV capsid targeting biases. As expected, we observed that the tropism profile of AAV.CAP-B10 in mice was shifted toward neurons and away from astrocytes when compared with AAV-PHP.eB. Transcriptomic analysis revealed that this neuronal bias is due mainly to increased targeting efficiency for glutamatergic neurons, which we confirmed by RNA fluorescence in situ hybridization. We further uncovered cell subtype tropisms of AAV variants in vascular and glial cells, such as low transduction of pericytes and Myoc+ astrocytes. Additionally, we have observed cell-type-specific transitory responses to systemic AAV-PHP.eB administration, such as upregulation of genes involved in p53 signaling in endothelial cells three days post-injection, which return to control levels by day twenty-five. The presented experimental and computational approaches for parallel characterization of AAV tropism will facilitate the advancement of safe and precise gene delivery vehicles, and showcase the power of understanding responses to gene therapies at the single-cell level.

A Universal Labeling Strategy for Nucleic Acids in Expansion Microscopy

Expansion microscopy (ExM) enables the nanoscale imaging of ribonucleic acids (RNAs) on a conventional fluorescence microscope, providing information on the intricate patterns of gene expression at (sub)cellular resolution and within spatial context. To extend the use of such strategies, we examined a series of multivalent reagents that allow the labeling and grafting of deoxyribonucleic acid (DNA) oligonucleotide probes in a unified approach. We show that the reagents are directly compatible with third-generation in situ hybridization chain reaction RNA FISH (fluorescence in situ hybridization) techniques while displaying complete retention of the targeted transcripts. Furthermore, we validate and demonstrate that our labeling method is compatible with multicolor staining. Through oligonucleotide-conjugated antibodies, we demonstrate excellent performance in ×4 ExM and ×10 ExM, achieving a resolution of ∼50 nm in ×10 ExM for both pre- and postexpansion labeling strategies. Our results indicate that our multivalent molecules enable the rapid functionalization of DNA oligonucleotides for ExM.

RNA imaging in bacteria

The spatiotemporal regulation of gene expression plays an essential role in many biological processes. Recently, several imaging-based RNA labeling and detection methods, both in fixed and live cells, were developed and now enable the study of transcript abundance, localization and dynamics. Here, we review the main single-cell techniques for RNA visualization with fluorescence microscopy and describe their applications in bacteria.

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.

Selected In Situ Hybridization Methods: Principles and Application

We are experiencing rapid progress in all types of imaging techniques used in the detection of various numbers and types of mutation. In situ hybridization (ISH) is the primary technique for the discovery of mutation agents, which are presented in a variety of cells. The ability of DNA to complementary bind is one of the main principles in every method used in ISH. From the first use of in situ techniques, scientists paid attention to the improvement of the probe design and detection, to enhance the fluorescent signal intensity and inhibition of cross-hybrid presence. This article discusses the individual types and modifications, and is focused on explaining the principles and limitations of ISH division on different types of probes. The article describes a design of probes for individual types of in situ hybridization (ISH), as well as the gradual combination of several laboratory procedures to achieve the highest possible sensitivity and to prevent undesirable events accompanying hybridization. The article also informs about applications of the methodology, in practice and in research, to detect cell to cell communication and principles of gene silencing, process of oncogenesis, and many other unknown processes taking place in organisms at the DNA/RNA level.

Runx1 and Runx3 drive progenitor to T-lineage transcriptome conversion in mouse T cell commitment via dynamic genomic site switching

Runt domain-related (Runx) transcription factors are essential for early T cell development in mice from uncommitted to committed stages. Single and double Runx knockouts via Cas9 show that target genes responding to Runx activity are not solely controlled by the dominant factor, Runx1. Instead, Runx1 and Runx3 are coexpressed in single cells; bind to highly overlapping genomic sites; and have redundant, collaborative functions regulating genes pivotal for T cell development. Despite stable combined expression levels across pro-T cell development, Runx1 and Runx3 preferentially activate and repress genes that change expression dynamically during lineage commitment, mostly activating T-lineage genes and repressing multipotent progenitor genes. Furthermore, most Runx target genes are sensitive to Runx perturbation only at one stage and often respond to Runx more for expression transitions than for maintenance. Contributing to this highly stage-dependent gene regulation function, Runx1 and Runx3 extensively shift their binding sites during commitment. Functionally distinct Runx occupancy sites associated with stage-specific activation or repression are also distinguished by different patterns of partner factor cobinding. Finally, Runx occupancies change coordinately at numerous clustered sites around positively or negatively regulated targets during commitment. This multisite binding behavior may contribute to a developmental "ratchet" mechanism making commitment irreversible.

Comparison of bias and resolvability in single-cell and single-transcript methods

Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.

Enhanced detection of expanded repeat mRNA foci with hybridization chain reaction

Transcribed nucleotide repeat expansions form detectable RNA foci in patient cells that contribute to disease pathogenesis. The most widely used method for detecting RNA foci, fluorescence in situ hybridization (FISH), is powerful but can suffer from issues related to signal above background. Here we developed a repeat-specific form of hybridization chain reaction (R-HCR) as an alternative method for detection of repeat RNA foci in two neurodegenerative disorders: C9orf72 associated ALS and frontotemporal dementia (C9 ALS/FTD) and Fragile X-associated tremor/ataxia syndrome. R-HCR to both G₄C₂ and CGG repeats exhibited comparable specificity but > 40 × sensitivity compared to FISH, with better detection of both nuclear and cytoplasmic foci in human C9 ALS/FTD fibroblasts, patient iPSC derived neurons, and patient brain samples. Using R-HCR, we observed that integrated stress response (ISR) activation significantly increased the number of endogenous G₄C₂ repeat RNA foci and triggered their selective nuclear accumulation without evidence of stress granule co-localization in patient fibroblasts and patient derived neurons. These data suggest that R-HCR can be a useful tool for tracking the behavior of repeat expansion mRNA in C9 ALS/FTD and other repeat expansion disorders.

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

Biological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Due to the technical limitations, spatial transcriptomics data suffers from only sparsely measured mRNAs by in-situ capture and possibly missing spots in tissue regions that entirely failed fixing and permeabilizing RNAs. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

Navigating across multi-dimensional space of tissue clearing parameters

Optical tissue clearing refers to physico-chemical treatments which make thick biological samples transparent by removal of refractive index gradients and light absorbing substances. Although tissue clearing was first reported in 1914, it was not widely used in light microscopy until 21th century, because instrumentation of that time did not permit to acquire and handle images of thick (mm to cm) samples as whole. Rapid progress in optical instrumentation, computers and software over the last decades made micrograph acquisition of centimeter-thick samples feasible. This boosted tissue clearing use and development. Numerous diverse protocols have been developed. They use organic solvents or water-miscible substances, such as detergents and chaotropic agents; some protocols require application of electric field or perfusion with special devices. There is no 'best-for-all' tissue clearing method. Depending on the case, one or another protocol is more suitable. Most of protocols require days or even weeks to complete, thus choosing an unsuitable protocol may cause an important waste of time. Several inter-dependent parameters should be taken into account to choose a tissue clearing protocol, such as: (1) required image quality (resolution, contrast, signal to noise ratio etc), (2) nature and size of the sample, (3) type of labels, (4) characteristics of the available instrumentation, (5) budget, (6) time budget, and (7) feasibility. Present review focusses on the practical aspects of various tissue clearing techniques. It is aimed to help non-experts to choose tissue clearing techniques which are optimal for their particular cases.

Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches.

A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ-compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.

Reciprocal Induction of MDM2 and MYCN in Neural and Neuroendocrine Cancers

MYC family oncoproteins MYC, MYCN, and MYCL are deregulated in diverse cancers and via diverse mechanisms. Recent studies established a novel form of MYCN regulation in MYCN-overexpressing retinoblastoma and neuroblastoma cells in which the MDM2 oncoprotein promotes MYCN translation and MYCN-dependent proliferation via a p53-independent mechanism. However, it is unclear if MDM2 also promotes expression of other MYC family members and has similar effects in other cancers. Conversely, MYCN has been shown to induce MDM2 expression in neuroblastoma cells, yet it is unclear if MYC shares this ability, if MYC family proteins upregulate MDM2 in other malignancies, and if this regulation occurs during tumorigenesis as well as in cancer cell lines. Here, we report that intrinsically high MDM2 expression is required for high-level expression of MYCN, but not for expression of MYC, in retinoblastoma, neuroblastoma, small cell lung cancer, and medulloblastoma cells. Conversely, ectopic overexpression of MYC as well as MYCN induced high-level MDM2 expression and gave rise to rapidly proliferating and MDM2-dependent cone-precursor-derived masses in a cultured retinoblastoma genesis model. These findings reveal a highly specific collaboration between the MDM2 and MYCN oncoproteins and demonstrate the origin of their oncogenic positive feedback circuit within a normal neuronal tissue.