Bringing Microscopy-By-Sequencing into View

Scalable spatial transcriptomics through computational array reconstruction

Spatial transcriptomics enables gene expression mapping within tissues but is often limited by imaging constraints. We present an imaging-free approach that reconstructs spatial barcode locations using molecular diffusion and dimensionality reduction. Validated against ground truth imaging, our method achieves high fidelity and scales to centimeter-sized tissues. This approach enhances spatial transcriptomics' accessibility and throughput, enabling large-scale studies without specialized imaging equipment.

Scalable imaging-free spatial genomics through computational reconstruction

Tissue organization arises from the coordinated molecular programs of cells. Spatial genomics maps cells and their molecular programs within the spatial context of tissues. However, current methods measure spatial information through imaging or direct registration, which often require specialized equipment and are limited in scale. Here, we developed an imaging-free spatial transcriptomics method that uses molecular diffusion patterns to computationally reconstruct spatial data. To do so, we utilize a simple experimental protocol on two dimensional barcode arrays to establish an interaction network between barcodes via molecular diffusion. Sequencing these interactions generates a high dimensional matrix of interactions between different spatial barcodes. Then, we perform dimensionality reduction to regenerate a two-dimensional manifold, which represents the spatial locations of the barcode arrays. Surprisingly, we found that the UMAP algorithm, with minimal modifications can faithfully successfully reconstruct the arrays. We demonstrated that this method is compatible with capture array based spatial transcriptomics/genomics methods, Slide-seq and Slide-tags, with high fidelity. We systematically explore the fidelity of the reconstruction through comparisons with experimentally derived ground truth data, and demonstrate that reconstruction generates high quality spatial genomics data. We also scaled this technique to reconstruct high-resolution spatial information over areas up to 1.2 centimeters. This computational reconstruction method effectively converts spatial genomics measurements to molecular biology, enabling spatial transcriptomics with high accessibility, and scalability.

Optics-free Spatial Genomics for Mapping Mouse Brain Aging

Spatial transcriptomics has revolutionized our understanding of cellular network dynamics in aging and disease by enabling the mapping of molecular and cellular organization across various anatomical locations. Despite these advances, current methods face significant challenges in throughput and cost, limiting their utility for comprehensive studies. To address these limitations, we introduce IRISeq (Imaging Reconstruction using Indexed Sequencing), a optics-free spatial transcriptomics platform that eliminates the need for predefined capture arrays or extensive imaging, allowing for the rapid and cost-effective processing of multiple tissue sections simultaneously. Its capacity to reconstruct images based solely on sequencing local DNA interactions allows for profiling of tissues without size constraints and across varied resolutions. Applying IRISeq, we examined gene expression and cellular dynamics in thirty brain regions of both adult and aged mice, uncovering region-specific changes in gene expression associated with aging. Further cell type-centric analysis further identified age-related cell subtypes and intricate changes in cell interactions that are distinct to certain spatial niches, emphasizing the unique aspects of aging in different brain regions. The affordability and simplicity of IRISeq position it as a versatile tool for mapping region-specific gene expression and cellular interactions across various biological systems.

Molecular pixelation: spatial proteomics of single cells by sequencing

The spatial distribution of cell surface proteins governs vital processes of the immune system such as intercellular communication and mobility. However, fluorescence microscopy has limited scalability in the multiplexing and throughput needed to drive spatial proteomics discoveries at subcellular level. We present Molecular Pixelation (MPX), an optics-free, DNA sequence-based method for spatial proteomics of single cells using antibody-oligonucleotide conjugates (AOCs) and DNA-based, nanometer-sized molecular pixels. The relative locations of AOCs are inferred by sequentially associating them into local neighborhoods using the sequence-unique DNA pixels, forming >1,000 spatially connected zones per cell in 3D. For each single cell, DNA-sequencing reads are computationally arranged into spatial proteomics networks for 76 proteins. By studying immune cell dynamics using spatial statistics on graph representations of the data, we identify known and new patterns of spatial organization of proteins on chemokine-stimulated T cells, highlighting the potential of MPX in defining cell states by the spatial arrangement of proteins.

Next-Generation Sequencing to Study the DNA Interaction

Next-generation sequencing (NGS) has transformed genomics by allowing researchers to sequence DNA and RNA at highest speed, accuracy, and cost-effectiveness. Researchers investigate DNA interactions with the help next-generation sequencing with a great deal of information. Over the last decade, NGS technologies have advanced significantly, with the development of several platforms, including Illumina, PacBio, and Oxford Nanopore, each offering distinct advantages and uses. The use of next-generation sequencing (NGS) has aided in the discovery of genetic variations, gene expression patterns, and epigenetic modifications connected with a variety of diseases, including cancer, neurological disorders, and infectious diseases. By identifying these regions, we can control the expression of genes, cellular signaling pathways, and other key biological processes. NGS is an effective method for researching DNA interactions that has completely transformed the area of genomics. NGS has also played an important part in personalized medicine, enabling the discovery of disease-causing mutations and the creation of targeted medicines. Finally, NGS has transformed the field of genomics, resulting in new discoveries and applications in medicine, environmental sciences, and other fields.

DNA-GPS: A theoretical framework for optics-free spatial genomics and synthesis of current methods

While single-cell sequencing technologies provide unprecedented insights into genomic profiles at the cellular level, they lose the spatial context of cells. Over the past decade, diverse spatial transcriptomics and multi-omics technologies have been developed to analyze molecular profiles of tissues. In this article, we categorize current spatial genomics technologies into three classes: optical imaging, positional indexing, and mathematical cartography. We discuss trade-offs in resolution and scale, identify limitations, and highlight synergies between existing single-cell and spatial genomics methods. Further, we propose DNA-GPS (global positioning system), a theoretical framework for large-scale optics-free spatial genomics that combines ideas from mathematical cartography and positional indexing. DNA-GPS has the potential to achieve scalable spatial genomics for multiple measurement modalities, and by eliminating the need for optical measurement, it has the potential to position cells in three-dimensions (3D).

Image recovery from unknown network mechanisms for DNA sequencing-based microscopy

Imaging-by-sequencing methods are an emerging alternative to conventional optical micro- or nanoscale imaging. In these methods, molecular networks form through proximity-dependent association between DNA molecules carrying random sequence identifiers. DNA strands record pairwise associations such that network structure may be recovered by sequencing which, in turn, reveals the underlying spatial relationships between molecules comprising the network. Determining the computational reconstruction strategy that makes the best use of the information (in terms of spatial localization accuracy, robustness to noise, and scalability) in these networks is an open problem. We present a graph-based technique for reconstructing a diversity of molecular network classes in 2 and 3 dimensions without prior knowledge of their fundamental generation mechanisms. The model achieves robustness by obtaining an unsupervised sampling of local and global network structure using random walks, making use of minimal prior assumptions. Images are recovered from networks in two stages of dimensionality reduction first with a structural discovery step followed by a manifold learning step. By breaking the process into stages, computational complexity could be reduced leading to fast and accurate performance. Our method represents a means by which diverse molecular network generation scenarios can be unified with a common reconstruction framework.

A DNA-nanoassembly-based approach to map membrane protein nanoenvironments

Most proteins at the plasma membrane are not uniformly distributed but localize to dynamic domains of nanoscale dimensions. To investigate their functional relevance, there is a need for methods that enable comprehensive analysis of the compositions and spatial organizations of membrane protein nanodomains in cell populations. Here we describe the development of a non-microscopy-based method for ensemble analysis of membrane protein nanodomains. The method, termed nanoscale deciphering of membrane protein nanodomains (NanoDeep), is based on the use of DNA nanoassemblies to translate membrane protein organization information into a DNA sequencing readout. Using NanoDeep, we characterized the nanoenvironments of Her2, a membrane receptor of critical relevance in cancer. Importantly, we were able to modulate by design the inventory of proteins analysed by NanoDeep. NanoDeep has the potential to provide new insights into the roles of the composition and spatial organization of protein nanoenvironments in the regulation of membrane protein function.