Visualizing Drosophila centrioles by expansion microscopy

Protein retention expansion microscopy (ExM) retains genetically encoded fluorescent proteins or antibody-conjugated fluorescent probes in fixed tissue and isotropically expands the tissue through a swellable polymer network to allow nanoscale (<70 nm) resolution on diffraction-limited confocal microscopes. Despite numerous advantages ExM brings to biological studies, the full protocol is time-consuming and can take multiple days to complete. Here, we adapted the ExM protocol to the vibratome-sectioned brain tissue of Xenopus laevis tadpoles and implemented a microwave-assisted protocol to reduce the workflow from days to hours. In addition to the significantly accelerated processing time, our microwave-assisted ExM (M/WExM) protocol maintains the superior resolution and signal-to-noise ratio of the original ExM protocol. Furthermore, the M/WExM protocol yields higher magnitude of expansion, suggesting that in addition to accelerating the process through increased diffusion rate of reagents, microwave radiation may also facilitate the expansion process. To demonstrate the applicability of this method to other specimens and protocols, we adapted the microwave-accelerated protocol to whole mount adult brain tissue of Drosophila melanogaster fruit flies, and successfully reduced the total processing time of a widely-used Drosophila IHC-ExM protocol from 6 days to 2 days. Our results demonstrate that with appropriate adjustment of the microwave parameters (wattage, pulse duration, interval, and number of cycles), this protocol can be readily adapted to different model organisms and tissue types to greatly increase the efficiency of ExM experiments.

Expansion Microscopy (ExM) is an innovative and cost-effective super-resolution microscopy technique that has become popular in cell biology research. It achieves super-resolution by physically expanding specimens. Since its introduction, ExM has undergone continuous methodological developments to enhance its resolution and labeling capabilities. However, ExM imaging often encounters sample drift during image acquisition due to the physical movement of the expanded hydrogel, posing a significant challenge for accurate image reconstruction. Despite many proposed experimental solutions to mitigate sample drift, a universal solution has yet to be established. In response to this challenge, we developed 3D-Aligner, an advanced and user-friendly image analysis tool designed to computationally correct drift in ExM images for precise three-dimensional image reconstruction and downstream quantification. We demonstrate that 3D-Aligner effectively determines and corrects drift in ExM images with different expansion rates and various fluorescently labeled biological targets, showcasing its capabilities and robustness in drift correction. Additionally, we validate the precision of 3D-Aligner by comparing drift values across different labeled targets and highlight the importance of drift correction in quantification of biological structures.

Analysis of long noncoding RNA (lncRNA) localisation at both the tissue and subcellular levels can provide important insights into the cell types that are important for their function. By applying new fluorescent in situ hybridisation technique called hybridisation chain reaction (HCR), we achieved a high-throughput lncRNA visualisation and evaluation of clinical samples. Assessing 1728 pairs of 16 lncRNAs and clear-cell renal-cell carcinoma (ccRCC) specimens, three lncRNAs (TUG1, HOTAIR and CDKN2B-AS1) were associated with ccRCC prognosis. Furthermore, we derived a new lncRNA risk group of ccRCC prognosis by combining the expression levels of these three lncRNAs. Examining genomic alterations underlying this classification revealed prominent features of tumours that could serve as potential biomarkers for targeting lncRNAs. We then derived combination of HCR with expansion microscopy and visualised nanoscale-resolution HCR signals in cell nuclei, uncovering intracellular colocalization of three lncRNA (TUG1, HOTAIR and CDKN2B-AS1) signals such as those located intra- or out of the nucleus or nucleolus in cancer cells. LncRNAs are expected to be desirable noncoding targets for cancer diagnosis or treatments. HCR involves plural probes consisting of small DNA oligonucleotides, clinically enabling us to detect cancerous lncRNA signals simply and rapidly at a lower cost.

Biologists have long been fascinated with the organization and function of intricate protein complexes. Therefore, techniques for precisely imaging protein complexes and the location of proteins within these complexes are critically important and often require multidisciplinary collaboration. A challenge in these explorations is the limited resolution of conventional light microscopy. However, a new microscopic technique has circumvented this resolution limit by making the biological sample larger, thus allowing for super-resolution of the enlarged structure. This 'expansion' is accomplished by embedding the sample in a hydrogel that, when exposed to water, uniformly expands. Here, we present a protocol that transforms thick expansion microscopy (ExM) hydrogels into sections that are physically expanded four times, creating samples that are compatible with the super-resolution technique structured illumination microscopy (SIM). This super-resolution ExM method (ExM-SIM) allows the analysis of the three-dimensional (3D) organization of multiprotein complexes at ~30-nm lateral (xy) resolution. This protocol details the steps necessary for analysis of protein localization using ExM-SIM, including antibody labeling, hydrogel preparation, protease digestion, post-digestion antibody labeling, hydrogel embedding with tissue-freezing medium (TFM), cryosectioning, expansion, image alignment, and particle averaging. We have used this approach for 3D mapping of in situ protein localization in the Drosophila synaptonemal complex (SC), but it can be readily adapted to study thick tissues such as brain and organs in various model systems. This procedure can be completed in 5 d.

The neuronal microtubule cytoskeleton underlies the polarization and proper functioning of neurons, amongst others by providing tracks for motor proteins that drive intracellular transport. Different subsets of neuronal microtubules, varying in composition, stability, and motor preference, are known to exist, but the high density of microtubules has so far precluded mapping their relative abundance and three-dimensional organization. Here, we use different super-resolution techniques (STED, Expansion Microscopy) to explore the nanoscale organization of the neuronal microtubule network in rat hippocampal neurons. This revealed that in dendrites acetylated microtubules are enriched in the core of the dendritic shaft, while tyrosinated microtubules are enriched near the plasma membrane, thus forming a shell around the acetylated microtubules. Moreover, using a novel analysis pipeline we quantified the absolute number of acetylated and tyrosinated microtubules within dendrites and found that they account for 65–75% and ~20–30% of all microtubules, respectively, leaving only few microtubules that do not fall in either category. Because these different microtubule subtypes facilitate different motor proteins, these novel insights help to understand the spatial regulation of intracellular transport.

Enhanced expansion microscopy to measure nanoscale structural and biochemical remodeling in single cells.

Amine-reactive esters of aromatic fluorescent dyes are emerging as imaging probes for nondescript staining of cellular and tissue architectures. We characterised the staining patterns of 14 fluorescent dye ester species with varying physical and spectral properties in the broadly studied human HeLa cell line. When combined with the super-resolution technique expansion microscopy (ExM) involving swellable acrylamide hydrogels, fluorescent esters reveal nanoscale features including cytoplasmic membrane-bound compartments and nucleolar densities. We observe differential labelling patterns linked to the biochemical properties of the conjugated dye. Alterations in staining density and compartment specificity were seen depending on the timepoint of application in the ExM protocol. Additional complexity in labelling patterns was detected arising from inter-ester interactions. Our findings raise a number of considerations for the use of fluorescent esters. We demonstrate esters as a useful addition to the repertoire of stains of the cellular proteome, whether applied either on their own to visualise overall cellular morphology, or as counterstains providing ultrastructural context alongside specific target markers like antibodies.

Tetra-gel expansion microscopy combined with superresolution imaging enables superior accuracy

Neuroengineering, the field of creating new tools for neuroscience, is an emerging field which includes the invention of technologies to activate and silence electrical activity in the brain, technologies to read out electrical activity from cells in the brain, and tools for mapping the brain. In this talk I will give an overview of technologies that recently emerged from our lab. First, recent optogenetic tools have begun to reach the physical limits of performance. The optogenetic molecules (opsins) Chronos and Chrimson enable activation of distinct neural populations with multiple colors of light, and Jaws is a red shifted inhibitor of neural activity that enables noninvasive neural silencing. Since opsins express all along cell membranes, light focused on one cell body will result in artifactual activation of multiple cells, whose processes are physically touching the neuron of interest. To tackle this problem, we designed a class of somatic opsins that express mainly at the cell body. This, in combination with holographic stimulation enables single cell optogenetics at millisecond temporal resolution. Activity sensors represent an area of great interest in neuroengineering. The voltage sensor Archon is an archaerhodopsin-based molecule with a high voltage sensitivity and brightness compared to its predecessors. The aforementioned cross talk problem also pertains to sensors: since cell-processes are touching cell bodies, the signal coming from a cell body could in principle originate from nearby cells. To solve this problem we developed a GCaMP6f molecule that is retained in the cell body only, called somaGCaMP6f. This molecule enables low crosstalk physiological imaging in mice and fish. Lastly, we have developed a technology that greatly facilitates the mapping of the brain. Optical super-resolution techniques are slow and costly, and accordingly do not scale well to large-scale brain circuitry. Instead of improving the resolving power of the microscope, we have found a way to physically expand biological specimens 4x-20x in linear dimension, in an isotropic fashion. This method, which we call expansion microscopy (ExM), enables the mapping of molecules of interest across cells and tissues of extended scale, and thus facilitates the analysis of neural circuits across scales of relevance to understanding brain function.

While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about whom to jail. We begin with a striking fact: the defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mug shot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: they are not explained by demographics (e.g., race) or existing psychology research, nor are they already known (even if tacitly) to people or experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional data set (e.g., cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our article is that hypothesis generation is a valuable activity, and we hope this encourages future work in this largely “prescientific” stage of science.

The study of breast cancer dormancy in the bone marrow is an exceptionally difficult undertaking due to the complexity of the interactions of dormant cells with their microenvironment, their rarity and the overwhelming excess of hematopoietic cells. Towards this end, we developed an in vitro 2D clonogenic model of dormancy of estrogen-sensitive breast cancer cells in the bone marrow. The model consists of a few key elements necessary for dormancy. These include 1) the use of estrogen sensitive breast cancer cells, which are the type likely to remain dormant for extended periods, 2) incubation of cells at clonogenic density, where the structural interaction of each cell is primarily with the substratum, 3) fibronectin, a key structural element of the marrow and 4) FGF-2, a growth factor abundantly synthesized by bone marrow stromal cells and heavily deposited in the extracellular matrix. Cells incubated with FGF-2 form dormant clones after 6 days, which consist of 12 or less cells that have a distinct flat appearance, are significantly larger and more spread out than growing cells and have large cytoplasm to nucleus ratios. In contrast, cells incubated without FGF-2 form primarily growing colonies consisting of>30 relatively small cells. Perturbations of the system with antibodies, inhibitors, peptides or nucleic acids on day 3 after incubation can significantly affect various phenotypic and molecular aspects of the dormant cells at 6 days and can be used to assess the roles of membrane-localized or intracellular molecules, factors or signaling pathways on the dormant state or survival of dormant cells. While recognizing the in vitro nature of the assay, it can function as a highly useful tool to glean significant information about the molecular mechanisms necessary for establishment and survival of dormant cells. This data can be used to generate hypotheses to be tested in vivo models.

Analysis of gene expression data sets is a potent tool for gene function prediction, cis-element discovery, and hypothesis generation for the model plant Arabidopsis thaliana, and more recently for other agriculturally relevant species. In the case of Arabidopsis thaliana, experiments conducted by individual researchers to document its transcriptome have led to large numbers of data sets being made publicly available for data mining by the so-called "electronic northerns," co-expression analysis and other methods. Given that approximately 50% of the genes in Arabidopsis have no function ascribed to them by "conventional" homology searches, and that only around 10% of the genes have had their function experimentally determined in the laboratory, these analyses can accelerate the identification of potential gene function at the click of a mouse. This chapter covers the use of bioinformatic data mining tools available at the Bio-Array Resource ( http://www.bar.utoronto.ca ) and elsewhere for hypothesis generation in the context of seed biology.

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with early diagnosis being critical for improving patient outcomes. This study addresses two key aspects of CRC research: understanding its etiology and prognostic biomarkers and exploring the role of artificial intelligence (AI) in generating hypotheses for early diagnosis. In the first project, we analyzed prognostic biomarkers in CRC, revealing a significant negative correlation between TP53 and CD56 mutations, which have opposing effects on patient survival. TP53, a tumor suppressor gene, is frequently mutated in CRC, while CD56, a glycoprotein involved in cell adhesion, may act as a cancer enhancer. Immunohistochemical (IHC) analysis of 2,923 CRC cases highlighted the potential of these biomarkers in predicting treatment outcomes. In the second project, we evaluated ChatGPT 4.0's capability to generate innovative hypotheses for overcoming challenges in early CRC diagnosis. ChatGPT produced 65 hypotheses across three areas: improving screening accuracy, addressing technological limitations, and identifying reliable biomarkers. While ChatGPT rated 25 hypotheses as "excellent," human evaluators rated only five as highly novel and feasible, emphasizing the need for human oversight in assessing practicality and clinical relevance. Experimental plans for selected hypotheses were developed, with one rated as "excellent" and others as "good" or "moderate." In the third project, we further examined ChatGPT’s ability to generate hypotheses, focusing on drug resistance in CRC treatment. ChatGPT was tasked with generating hypotheses to address five major mechanisms of drug resistance, including genetic mutations, drug transport proteins, alterations in signaling pathways, tumor-associated macrophages (TAMs), and tumor heterogeneity. 68 hypotheses were generated, with 9 rated as novel, 21 as relatively novel, and 38 as not novel. ChatGPT provided experimental designs for each hypothesis, but human evaluators found that AI-generated hypotheses often lacked consideration of key biological constraints and feasibility challenges, reinforcing the necessity of human validation. Together, these studies highlight the importance of biomarker research in understanding CRC progression and the potential of AI as a complementary tool in hypothesis generation. While AI demonstrates remarkable creativity and novelty, human expertise remains essential for evaluating feasibility and translating hypotheses into clinically actionable solutions. This research underscores the need for further validation and collaboration between AI and medical researchers to advance early CRC diagnosis and improve patient outcomes.

Analysis of large-scale gene expression data sets is proving to be a powerful tool for gene function prediction, cis-element discovery and hypothesis generation using Arabidopsis thaliana. Public initiatives led by the AtGenExpress Consortium and experiments conducted by individual researchers to document the transcriptome of Arabidopsis thaliana have led to a large numbers of data sets being made publicly available for data mining by so-called "electronic northerns", co-expression analysis and other means. Given that approximately 50% genes in Arabidopsis have no function ascribed to them by "traditional" homology searches, and that only around 10% of the genes have had their function confirmed in the laboratory, these analyses can accelerate the identification of potential gene function with a mouse-click. This chapter covers the use of data mining tools available at the Bio-Array Resource (www.bar.utoronto.ca) for hypothesis generation in the context of plant hormone biology.

BackgroundTranslational research platforms share the aim of promoting a deeper understanding of stored data by providing visualization and analysis tools for data exploration and hypothesis generation. However, such tools are usually platform bound and are not easily reusable by other systems. Furthermore, they rarely address access restriction issues when direct data transfer is not permitted. In this article, we present an analytical service that works in tandem with a visualization library to address these problems.FindingsUsing a combination of existing technologies and a platform-specific data abstraction layer, we developed a service that is capable of providing existing web-based data warehouses and repositories with platform-independent visual analytical capabilities. The design of this service also allows for federated data analysis by eliminating the need to move the data directly to the researcher. Instead, all operations are based on statistics and interactive charts without direct access to the dataset.ConclusionsThe software presented in this article has a potential to help translational researchers achieve a better understanding of a given dataset and quickly generate new hypotheses. Furthermore, it provides a framework that can be used to share and reuse explorative analysis tools within the community.