The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing‐sensitive indicators in acute cardiac care.Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data‐based solutions that automatically extract and help interpret data from electronic health records.This is a deductive descriptive study that followed the theory of value‐added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free‐text format.One thousand six hundred seventy‐six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality.Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice.Knowledge‐based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real‐time big data for improved data access and interpretation to better support nursing management in quality assessment. From early on, concerns regarding the accurate portrayal of the complexity of care and data requirements for comprehensive quality assessment as well as accuracy, comparability and timeliness of extracted data have been presented  and the decree on patient status and rights (758/1992), all necessary and comprehensive information regarding the arrangement, planning, execution and monitoring of good patient care need to be documented in this EHR system. This information includes preliminary information regarding the patient's health status, illnesses or injuries, observations made by the caretakers, laboratory and imaging results as well as care measures to restore or maintain the patient's health or alleviate suffering. Entries made in the EHRs must be clear and understandable, containing only generally established concepts and abbreviations.The documentation in the EHRs follows a title structure where predefined classifications are complemented using narrative free text. All health care professionals make entries to the system following codes and standards specific to their own professional groups. For nurses, the system allows to document patient care systematically by phase of nursing process. Structured data elements, such as nursing diagnosis, interventions, outcomes, intensity and discharge summary, follow the Finnish Care Classification (FinCC) system as presented by the Finnish institute for health and welfare  admitted and treated at the acute cardiac care units of the cardiac centre during January 2020. The data collection point was selected to ensure the most recent data but to avert the possible skewness caused by the global outbreak of COVID‐19. The data included all clinical care entries made in the centre, such as free text nursing and physician notes as well as structured clinical measurements, patient evaluations and laboratory results from all units. Only operating room reports, intensive care nursing narratives and radiology images from the cardiac centre were excluded.The data were collected from patients (n = 2673) and measurements (n = 2336), physician notes (n = 527), diagnostic notes (n = 377) and laboratory results (n = 347).The sample consisted of the EHRs of 230 patients. To ensure as wide, versatile and reliable picture as possible regarding patient care, all available entries made in the EHR were included in this study. Only including one source of the documentation, such as the nursing narratives alone, would not provide a comprehensive picture of the care provided, as the documentation in the EHR is done in a multi‐professional way with all different perspectives complementing each other. The included records held 540 care episodes lasting from one to 13 days. They contained 6867 notes, including but not limited to nursing notes  2016/679 of the European Parliament and of the Council, the Data Protection Act (1050/2018) and the Finnish decree on patient records (298/2009). The EHR data used in this study were pseudonymized, and patients or hospital units could not be identified from the results. The data were securely stored on, and accessed through, the servers of the hospital district. The data were managed following a confidentiality agreement.‐guidelines  women and 132 (57%) men born between 1923 and 1995, ages ranging from 25 to 97 years (standard deviation 41 years). In total, 1676 expressions related to care quality were extracted of which 1235 (73.7%) originated from free text and 441 (26.3%) from structured notes.established expressions related to nursing care quality and unestablished expressions related to nursing care quality. Established expressions provided straightforward information directly related to nursing care quality that could be utilized as is or with minor adjustments in a tool assessing care quality. Unestablished expressions, in turn, provided information indirectly related to nursing care quality of which more research is still needed before its application in care quality assessment. Table The value‐adding analysis was concretized as an interpretative inductive analysis. The expressions were divided into two categories: positive descriptions, neutral descriptions and negative descriptions of patient status related to nursing care quality. These categories described the informational value of the extracted expressions, distinguishing between negative, neutral and positive care quality values. Positive descriptions were expressions indicating the absence of symptoms or adverse events as well as the occurrence or increase of general wellbeing and satisfaction. Negative descriptions, in turn, were expressions indicating increase or occurrence of adverse events, patient deterioration, symptoms or dissatisfaction. Neutral descriptions did not contain the aforementioned changes in patient's baseline condition or satisfaction. Table Additionally, the value adding analysis resulted in dividing all expressions extracted from the EHR's into one of the following three categories: 3.1Established expressions related to nursing care quality described experiences of pain, adverse events and patient satisfaction, as described in Figure experience of pain included expressions extracted from free text  and structured notes , being eminently the largest category identified with 662  different expressions. The expressions described the existence, nature and change of experienced pain. Expressions also contained information regarding the location of the pain, such as headache, chest pains, stomach ache or sore throat. The existence of pain was described negatively as experiencing (n = 129) or positively as not experiencing (n = 219) pain in free text notes. Neutral descriptions (n = 11) of existence indicated the patient having the pain under control, but also contained diagnostic entries extracted from the structured notes. The nature of pain included expressions describing tenderness, soreness, location or radiance of pain, but also the intensity of pain, indicated verbally and using pain rating metrics, such as the Visual Analogue Scale. A total of 197 expressions describing the nature of the experience of pain were extracted from structured notes (44.6% of structured entries), containing numerical information of pain during rest or motion. The expressions related to a change in pain perception described an increase or persistence of pain (n = 18), but also a decrease or discontinuation of pain (n = 27).The subcategory describing patients' n = 59, 4.0% of all) was mainly described in free text . Patient falls and fall related injuries described both the occurrence of the fall and details regarding the injury. All these expressions were extracted from free text notes . Expressions describing the occurrence (n = 36) were negative verbs indicating the patient falling or slipping during hospitalization. Patient fall related injuries contained information on both the obtaining (n = 11) but also the avoidance (n = 7) of bruises, dints, tears or undefined injuries. Medication errors were among the smallest subcategories with merely three expressions describing occurred errors in the medication process extracted from free text notes. These expressions were all negative, describing defects both in the administration and the documentation of the medication process. Pressure injuries were the smallest subcategory with two expressions describing tissue integrity with one expression being extracted from structured and the other from free text notes.The subcategory regarding adverse events  were extracted from free text notes (2.2% of free text entries). They were expressions as described by the nurse or as voiced by the patient. They presented negative descriptions (n = 9) of patients being annoyed, frustrated, confused, resentful or agitated, but also neutral expressions (n = 3) of being pro‐treatment and positive descriptions (n = 15) of being generally pleased.All expressions in the subcategory 3.2Unestablished expressions related to the nursing care quality presented patients' physical, perceived, functional and psychosocial health, as described in Figure physical health  contained expressions extracted both from structured  and free text  notes. Signs of infection were predominantly extracted from structured notes . They contained information on a patient's body temperature, but also laboratory results on bacterial colonization of wounds, blood and urine as well as C‐reactive protein (CRP), a common indicator used for detecting inflammation from blood. Expressions extracted from free text notes also described elevations and decreases of CRP, patient's body temperature and fever. The presence of infection contained expressions indicating the presence (n = 21) or absence (n = 11) of an undefined infection, symptoms related to an infection, wound infection, hospital acquired infection or sepsis. Neutral expressions (n = 2) were diagnostic entries extracted from structured notes. All expressions related to patients' physical health were connected to hospital acquired infections, but only one straight mention to an infections origin as hospital acquired was extracted, with the rest being merely context related.The subcategory regarding patients' perceived health  were all extracted from free text notes  of which nearly 75% (n = 240) were positive descriptions. A patient's perceived health was related to his or her well‐being, coping and symptoms. Positive descriptions (n = 237) of well‐being included improvement in a patient's condition and generally feeling well. Neutral expressions (n = 30) described the patient feeling moderate or normal. Expressions describing the patient feeling ill or getting worse were perceived as negative (n = 18). Approximately 90% (n = 30) of the expressions describing coping were negative descriptions of patient feeling out of strength, exhausted, tired or weary. Symptoms, in turn, described mainly neutrally (n = 5) with the patient having no symptoms or positively (n = 3) as the decrease of symptoms.Expressions in the subcategory covering expressions describing the patients' psychosocial health  included expressions of a patient's cognition, mood and feelings extracted from free text notes  except for one structured entry. Cognition was generally described with neutral expressions (n = 114) of the patient behaving appropriately or being oriented to time and place. No positive expressions describing a patient's cognition were identified. Expressions about mood included positive descriptions (n = 18) of the patient being in a good mood and feeling high‐spirited to negative descriptions (n = 14) of the patient being tearful or seeming stressed, anxious, or feeling low. An expression extracted from structured notes presented a scored depression scale. Feelings were described as mainly negative (n = 16), with the patient expressing agitation, fear, sadness and worry.The subcategory regarding the patients' functional health  were related to a patient's general status and ability to cope at home. Expressions describing the general status of patients were mainly perceived as good with positive descriptions (n = 40). In contrast, all descriptions (n = 9) regarding patients' abilities to cope at home were negative. All expressions describing patients' functional health originated from free text notes .The subcategory of expressions describing patients' 4The results present an overview of EHR‐based information related to nursing care quality from three differing standpoints that need to be considered in the development and implementation of real‐time information systems for care quality assessment. These include (1) a division between established and unestablished expressions, (2) a presentation of extracted information through their informational value and (3) an acknowledgement of the potential of establishing expressions of different structure in EHRs .The first standpoint indicates the degree of readiness of nursing‐sensitive indicators to be used in care quality assessment. Previous research presents ample evidence of the use of nursing‐sensitive indicators corresponding to the established expressions found in our study, such as experiences of pain or prevalence of pressure injuries  as well as free text nurse evaluations and numerical patient evaluations (n = 458). The study's results were in line with previous research stating that as a data source, free text notes offer invaluable information related to nursing care quality not available in structured notes  issued by University of Turku Ethics Committee for Human sciences .


Spatial transcriptomics is a newly emerging field that enables high‐throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to “in situ” biology, spatial transcriptomics can provide transcriptome‐scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi‐omics and their expanding roles in biomedical applications are emphasized. Spatial transcriptomics enables high‐throughput investigation of spatial localization of transcripts and related analyses in various applications for biological systems. This review provides a directed overview of the rapidly evolving field of spatial transcriptomics emphasizing technical insights, applications, and future directions in the fields of neuroscience, cancer studies, and translational biomedicine. Another example that illustrates the importance of spatial organization is cancer tissue, in which cells actively interact with the surrounding tumor microenvironment to generate suppressive conditions that block the action of immune cells, thereby bypassing immune defense mechanisms and facilitating proliferation. To understand the complexity of biological systems ranging from various physiological phenomena to the pathological principles of diseases, it is necessary to assess the functions of individual cells and their interactions to orchestrate complex functions of tissues and organs. Strategies for examining these biological principles include exploring cells that exist in the tissue (cell‐type inventory) and their spatial arrangement and interaction with each other .Each cell in a multicellular organism interacts with the surrounding environment. Stem cells differentiate during development primarily through cell‐to‐cell interactions and subsequent signaling, which is governed by the relative positions of cells within the embryo.1, 2 Furthermore, recent advances in single‐cell transcriptome analysis techniques allow researchers to analyze transcriptomes with unprecedentedly high throughput and single‐cell resolution. Despite the improvements in sequencing technologies, single‐cell transcriptome analysis techniques are still limited in that all information related to the spatial organization of cells in the tissue is permanently lost owing to tissue dissociation.Molecular expression patterns of diverse biological states have been analyzed by RNA sequencing owing to the efficient and sensitive detection of RNA by the simple amplification of nucleic acids by polymerase chain reaction (PCR) and high‐throughput readouts by next‐generation sequencing (NGS). RNA sequencing has served as one of the major approaches for basic biological research and clinical diagnosis because the transcriptome serves as a blueprint of the proteome, the actual functional proteins of the cell, and therefore reflects the current cellular state.7 Furt Therefore, in this review article, we instead focus on providing a comprehensive review of the technical differences between sequencing‐ and imaging‐based methodologies, recent advances in the spatial approach utilizing spatial data and their computational interpretation, and current research and future perspectives in various fields of application in biological studies, including neuroscience and cancer, and in biomedical and clinical studies of disease.Spatial transcriptomics provides information on the spatial distribution of gene expression profiles, thereby elucidating interesting features previously not revealed by single‐cell RNA sequencing methods that lack spatial information. Several high‐quality review papers already exist on spatial transcriptomics.14 The22.1 These smFISH methods facilitated the detection of target RNAs with high specificity and sensitivity at the single‐molecule level. smFISH also provides single‐cell and subcellular resolution with optimized protocols for cell cultures and thin (<20 µm) tissues respectively. Recently, spatial applications of smFISH have demanded more scalable platforms in order to investigate biologically meaningful dimensions according to their detection targets. Fast cycling of probe hybridization allows the spatial investigation of relatively large areas with high specificity and sensitivity when targeting 20–40 highly expressed marker genes  without barcoding. However, the limited multiplexing capacity of smFISH, caused by the spectral overlap of fluorescent dyes, precludes efficient transcriptome‐level spatial investigations when creating a profile of the transcriptional status of cells in a tissue.To detect transcripts from cells, pioneering studies reported single‐molecule fluorescence in situ hybridization (smFISH) methods that employed in situ hybridization of reverse‐complementary oligo probes conjugated with fluorophores.16 The Initial efforts for multiplexed RNA profiling focused on improving the barcoding capacity using 1) spectral barcoding by super‐resolution microscopy and 2) sequential barcoding using DNase I , precluding the scalable application of this method to real‐world samples. An improved protocol for a sequential barcoding scheme by DNase I dramatically enhanced the performance of repeated probing for multiplexed RNA FISH and simplified the experimental procedure so that multiplexed barcoding experiments could be executed with an ordinary epifluorescence microscope. Although the initial report on the DNase I‐based sequential barcoding scheme only presented its application to yeast cells, the concept of multi‐round imaging and registered barcode calling for smFISH‐based experiments was continuously adapted to the later versions of highly multiplexed FISH experiments.Separating the fluorescence signals obtained from individual transcripts is the key to highly increasing the multiplexing capacity. This idea has been previously implemented for DNA, but in most cases, these attempts were close to the preliminary, proof‐of‐concept stage.19 Ini and sequential FISH (seqFISH), Figure  To evenly distribute the “loading” of gene expression in each fluorescence channel, careful estimation of expression from bulk sequencing results is employed to design orders of imaging and to determine which genes can be co‐imaged. Super‐resolution imaging by localization microscopy was employed to resolve more transcripts in each diffraction‐limited spot, which finally enabled true transcriptome‐level detection in the order of ten thousand transcripts per cell (seqFISH+)   is an electrophoresis‐aided large tissue RNA sampling and multiplexed FISH study that attempts to reduce data acquisition time by transferring RNA from 10‐µm‐thick tissue to the plane of an RNA capture slide for imaging the transcriptome of thick tissues with an epifluorescence microscope.In 2015 and 2016, continued efforts to develop highly multiplexed FISH methods increased the number of detected genes from a dozen to several hundred and then up to one thousand transcripts per cell [multiplexed error‐robust FISH (MERFISH)22 and) Figure .[26]) Figure .s Figure . Although FISSEQ‐synthesized cDNA uses random hexamers and direct reading of cDNA sequences by SOLiD chemistry to avoid target selection bias and enable de novo discovery of expressed genes, it also shows low efficiency of gene detection owing to overwhelming occupancy of the fluorescence channels by signals originating from rRNAs  0.8 air for HybISS; 40×, NA 1.3 oil for STARmap), which enables the investigation of relatively large‐sized tissues. Directly targeting RNA with padlock probes to eliminate inefficient reverse transcription during cDNA synthesis can enhance the efficiency of ISS [barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS) and hybridization‐based RNA ISS (HybrISS)]. Moreover, commercialized options for imaging‐based spatial transcriptomics technologies will soon be available, including MERSCOPE , Xenium , products from Spatial Genomics , and GeoMX and CosMX from NanoString .Since both FISH and ISS use pre‐designed probes to label target transcripts, only preset genes related to a certain experimental hypothesis are profiled, and this preset repertoire of target genes leads to biased detection of transcriptomes.36 Alts Figure . Additio2.2 The lack of spatial context in transcriptomic data, however, has led to the development of new techniques capable of encoding spatial information as nucleotide barcoding sequences. The spatial location of transcripts can be recovered using NGS by incorporating spatial barcodes to construct RNA sequencing libraries. NGS‐based spatial transcriptomics approaches have superior throughput compared to imaging‐based methodologies because the entire transcript information is detected and massively parallel‐processed. Additionally, NGS‐based approaches do not require a pre‐targeted list of genes because the retrieval of transcript spatial distribution is accompanied by cDNA synthesis in an unbiased and non‐targeted manner.NGS platforms have shown unprecedented success in producing massive genomic sequencing data because of their superior sequencing capacity at a substantially reduced cost.43 The LCM‐seq, and the more recent spatial‐histopathological examination‐linked epitranscriptomics converged to transcriptomics with sequencing (Select‐seq), combine LCM with Smart‐seq2 for polyA+ RNA sequencing of select cell populations that are identified for dissection by immunohistochemistry (IHC). Tomo‐seq uses a tomography‐inspired sectioning approach to resolve the transcriptome of anatomical ROIs. Through a combination of LCM‐seq and Tomo‐seq, Geo‐seq  enables 3D transcriptome analysis of select ROIs and single cells. For an alternative to physical dissection, methods such as NICHE‐seq, ZipSeq, Light‐seq, and GeoMX Digital Spatial Profiling instead use optical methods to mark regions or cells of interest. The strength of these regioselective sequencing methods is easy implementation and sensitive sequencing with small sample sizes (<200 cells) where a clear spatial ROI is already defined by IHC staining. Additionally, LCM with SMART‐3Seq and GeoMX have demonstrated their applicability for formalin‐fixed paraffin‐embedded (FFPE) samples. Recent investigations employed GeoMX to study archived FFPE human cancer samples. Although there are notable advantages to LCM methods, they typically have limited spatial resolution and throughput compared to recent spatial barcoding NGS techniques.Early approaches for including the spatial context in NGS performed microscopy‐guided selection and isolation of regions of interest (ROIs) for analysis by full‐read RNA sequencing. Laser capture microdissection (LCM) is a widely used strategy for the physical dissection of tissue ROIs by laser cutting.45 LCM This new method has been used to locate transcripts by transferring RNA molecules to a glass slide coated with poly‐T primers containing a unique molecular identifier (UMI) and a spatial barcode using ≈100 µm pixel size resolution  has subsequently demonstrated a spatial resolution of 2 µm using a silicon wafer  includes a modified polymer surface for clustering to minimize gaps between the barcoded pixels. This polymer‐based surface can generate continuous features by tightly distributed mRNA capture oligos. Subsequently, the spatial barcode is incorporated into the cDNAs by reverse transcription, and the cDNA sequence and spatial barcode are simultaneously analyzed by NGS. Spatial enhanced resolution omics sequencing (Stereo‐seq) also utilizes NGS devices to manufacture spatial barcode arrays using MGI's DNA nanoball sequencing (DNBseq) chemistry ]. Additional RNA fixation helps to improve RNA retention during tissue clearing (EDC–CLARITY). MERFISH, an imaging‐based multiplexed FISH method, is also compatible with hydrogel‐embedded tissues and was previously employed to reduce autofluorescence signals from tissues. The tissue clearing method stabilization under harsh conditions via intramolecular epoxide linkages to prevent degradation (SHIELD) utilizes a polyepoxy crosslinker as a fixative and was originally developed to protect multimodal biomolecules and tissue architecture. RNA molecules are easily detected by FISH hybridization chain reaction (HCR) after tissue clearing by SHIELD; therefore, SHIELD could serve as a 3D spatial multi‐omic tissue processing and imaging platform.Tissue clearing techniques enable direct observation of 3D structures from transparent thick‐tissue blocks prepared by optical clearing and refractive index matching.66 In  swellable hydrogel‐embedded tissues expand and magnify their structure, which improves their effective resolution by increasing intermolecular distance. Following subsequent optimization of ExM using swellable hydrogels and a nucleic acid crosslinker (Label‐X), expansion FISH (ExFISH) has demonstrated multiplexed FISH in expanded biological samples. Further optimization of the ExFISH method led to expansion‐assisted iterative FISH (EASI‐FISH), which can quantitatively analyze relatively large tissue volumes (up to 300 µm thickness before expansion) to enable true single‐cell transcriptomics by investigating multiple layers of intact cells by imaging. Development of in situ sequencing using expansion methods yielded ExSeq for combining tissue expansion with in situ sequencing methods such as FISSEQ. Expansion sequencing (ExSeq) has further improved the resolution of FISSEQ by adding a physical expansion, resulting in better coverage of transcript detection in volume. Unified ExM (UniExM) employs glycidyl methacrylate (GMA) as a nucleic acid crosslinker, which dramatically reduces experimental costs. Using GMA improves nucleic acid retention in polyacrylamide‐based hydrogels and provides excellent results when applied in multiplexed FISH in situ sequencing with expansion. These 3D tissue investigation approaches may play significant roles in studying transcriptomes and multi‐omes of tissues and organoids as multi‐cell layer spatial information is necessary to understand the complex behavior of biological systems.Tissue expansion techniques allow super‐resolution imaging with diffraction‐limited light microscopy by physically expanding hydrogel‐embedded tissues. In expansion microscopy (ExM),71 swe33.1Highly multiplexed FISH and in situ sequencing methods are based on imaging using fluorescence microscopy to locate single transcripts at subcellular resolution. Although the spatial resolution varies among techniques, imaging‐based methods can provide sub‐micron resolution when employing a high‐NA objective during imaging. When additional information is obtained using probes , the spatial data can be projected onto the cell contours by cell segmentation. Although these imaging‐based methods provide high spatial resolution, this feature inevitably limits the size of tissue coverage. The tissue area captured within a single imaging cycle is limited by the field of view of the objective, and the imaging time depends on the signal‐to‐noise ratio and target resolution. In addition, detecting broad features from various transcriptional profiles requires multiple rounds of imaging followed by probe hybridization or enzymatic reactions for ISS. Acquiring multiple images includes time‐consuming steps, such as probe replacement between each image, and requires a substantial amount of time that limits the acquisition of information over a large area.Optical crowding in raw images is another technical hurdle in imaging‐based methods. Considering that one cell is typically populated with thousands of transcripts, the point‐spread functions of the fluorescence signals emitted from these transcripts often overlap when simultaneously detecting multiple species in a single acquisition step. Highly multiplexed FISH methods can detect a large number of transcripts owing to their excellent hybridization efficiency; however, consequently, signal overlap makes the deconvolution of barcodes difficult. MERFISH and seqFISH+ can resolve optical crowding issues by reducing the number of transcripts detected in a single image providing increased space for barcode combination. However, this approach directly impacts imaging time by increasing the number of imaging rounds and time required to image large combinations of barcodes. In ISS‐based methods, optical crowding is less problematic because these methods exhibit low detection efficiency when converting transcripts to cDNAs in situ. FISSEQ detects ≈0.2–1% of transcripts owing to the low yield of in situ reverse transcription and cDNA synthesis. Other methods utilizing pad‐lock probes that hybridize to their target RNA species require enzymatic ligation that results in low detection compared to those of multiplexed FISH methods.3.2NGS‐based methods generate massive amounts of raw data owing to the high throughput of NGS devices. Initial capturing of mRNAs using barcode oligos and subsequent cDNA synthesis does not require specialized equipment. An additional benefit is that NGS‐based methods use poly‐T oligos for unbiased capture of mRNAs, and therefore do not require predefined probe panels to label target RNA. This feature allows de novo discovery of RNAs with statistically significant differences in spatial distribution.Owing to the limited spatial resolution, deciphering spatial barcodes to infer the spatial origin of transcripts cannot provide their cellular origin. The mRNA‐capturing oligos contain specific spatial barcode sequences that represent specific regions in 2D coordinates to distinguish the spatial origin of transcripts. A set of transcriptomes sharing the same spatial barcode constitutes transcriptome information for each tissue region, such as pixels in an image, and the arrangement of these pixels eventually represents the spatial distribution of the entire tissue transcriptome. However, each pixel specified by the same spatial barcode in the 2D coordinates does not provide the actual cellular boundaries required to reconstruct single‐cell‐level information. Therefore, different technologies require different information processing. If the size of a pixel is greater than that of a single cell, the transcript of one pixel would be a mixture of the transcripts of several cells and vice versa. Although sci‐space and XYZeq enable single‐nuclear distinction of the transcriptome by dissociating nuclei, the spatial resolution of nuclear location is still limited by pixel size.3.3Tissue clearing and expansion techniques can enhance the effective resolution of 3D spatial transcriptomic investigations for future biomedical applications. Despite the valuable information that can be extracted through these methods, there are remaining points of optimization that continue to limit widespread implementation. To our best knowledge, the combinatorial barcoding scheme has not been applied for increasing multiplexity due to technical challenges in registration of fluorescence signals from large 3D raw images. As a result, the processing time required for repeated 3–4 color imaging and staining/destaining cycles with linearly increasing numbers of gene targets is lengthy for capturing large area samples. Additionally, the increased resolution and addition of volumetric analysis inherently result in immense data sets in biomedical applications. The current analysis platforms require considerable improvement before they are capable of handling data volumes in the order of a hundred terabytes to several petabytes. Following optimization of these aspects of tissue clearing and expansion techniques, large biomedical datasets with enhanced spatial resolution can be leveraged to uncover additional transcriptomic information from complex and crowded environments.4FigureAfter successfully implementing spatial transcriptome experiments, interpreting large amounts of raw gene expression matrices is challenging. Each method for spatially resolved transcriptomics generates data of various scales, resolutions, and modalities, according to their working mechanisms, to capture and identify transcripts. Therefore, analytical pipelines that process raw data should consider these differences for successful interpretation. Additionally, limited information on current spatial transcriptome data is sometimes analyzed together with the existing information. In this section, we introduce useful analysis pipelines and algorithms for handling data from spatial transcriptomics Figure3. Sinc4.1xy plane images. These images are typically subjected to various filters, such as Laplacian of Gaussian filters to remove noise and thresholding for dot detection. Initially, coarse‐grained registration among color channels and imaging rounds is applied with reference to specified registration markers . One significant challenge in assigning barcodes is to determine the acceptable error range in terms of pixel distances for successful barcode calling with minimal error. When the number of barcoding genes is small, less accurately registered fluorescence signals may be tolerated without substantially affecting the success rate in barcode calling. Some methods such as MERFISH have error correction schemes to reduce the effect of incorrect barcode assignment. However, transcriptome‐level barcoding requires raw images obtained through super‐resolution imaging modalities to further resolve multiple transcripts located within diffraction‐limited spots. With this level of complexity, barcode calling rates can be significantly reduced compared to those of experiments with dozens and hundreds of target genes, and the resulting analysis scheme inherently yields decreased RNA detection efficiency. Imaging‐based methods also involve image processing and error correction steps for chromatic correction between fluorescent channels and image registration between multiple rounds. For example, initial MERFISH papers employed only the 647 channel to avoid chromatic aberration.Regardless of their working principles, all imaging‐based methods for spatial transcriptomics require three crucial steps in raw data processings: 1) registration of barcoded fluorescence signals from raw images, 2) barcode calling or processing to assign target RNA reads, and 3) efficient cell segmentation to assign barcoded dots to each cell. Prior to further processing, raw images are stitched and maximum‐projected along the z‐axis to generate 2D It is important to note that all of the correction steps mentioned earlier are typically performed with specific ad hoc parameters optimized for a particular spatial transcriptomics method and setup, which makes the raw image data processing pipeline difficult to generalize. Therefore, it may be more practical for researchers to begin their imaging‐based spatial transcriptomic analysis with pre‐processed data generated by commercial or pre‐optimized protocols. The following sections will focus more on reviewing data‐processing techniques for NGS‐based methods.Due to technical limitations of currently available NGS‐based spatial transcriptomic methodologies, raw data may contain noise from various sources and often suffer from signal loss. As a result, a low signal‐to‐noise ratio (SNR) can compromise the accuracy of data analysis. To address this issue, preprocessing methods for noise reduction have been explored.  Figure . Due to ] Figure . Recently, using deep‐learning models, morphological features have been extracted using spatial gene expression patterns in combination with hematoxylin and eosin (H&E)‐stained images. The extracted features can be compared among spatial data spots to complete the normalization of spatial data, which is called Spatial Morphological gene Expression Normalization .Normalization of spatial transcriptome data is crucial for comparing gene expression between spots or genes Figure  and is p80 Rec4.24.2.1Single‐cell and single‐nucleus sequencing databases have grown rapidly in terms of higher throughput and data quality. When consolidating these rich databases, it is important to predict the spatial distribution of transcripts by combining relatively crude quality spatial data Figure . Spatial allows seamless integration between single‐cell sequencing data and multiplexed FISH data by comparing the expression of landmark genes in single‐cell sequencing data with their spatial distribution in multiplexed FISH data. The expression data obtained from this process is represented by a bimodal mixture model. Spatial backmapping adopts a similar approach, but comparisons are based on specificity‐weighted mRNA profiles, which indicate the expression of each gene in a specific cell relative to that of all other cells.Seurat82 all imputes missing genes in multiplexed FISH data using single‐cell data via principal component analysis (PCA)‐based domain adaptation and k‐nearest‐neighbor regression. Gene imputation with Variational Inference (gimVI) method also performs imputation using a deep generative model.Unlike these methods focusing on the spatial reconstruction of single‐cell sequencing data, spatial gene enhancement (SpaGE)78 imp integrates single‐cell and spatial data, as well as chromatin accessibility and immunophenotyping data. Integrative analysis of multi‐omics at single‐cell resolution (GLUER) integrates single‐cell sequencing data with transcript and protein spatial data captured by highly multiplexed methods such as co‐detection by indexing (CODEX) and MERFISH. Both DEEPsc and Tangram employ single‐cell sequencing data for spatial reconstruction and can consolidate with spatial data obtained by spatial transcriptomics methodologies without limitations; therefore, both multiplexed FISH and NGS‐based data can be used as inputs. GLUER, DEEPsc, and Tangram have recently started using deep neural network models for improved prediction and data integration.Recently, data for the integration of spatial contexts is more diversified, and deep learning is widely employed. Seurat v384 int4.2.2 but treating deconvolution by substituting it as a domain adaptation task is also plausible. These methods initially define cell type as discrete or solid. Based on this hypothesis, they carry out cell‐type inference. However, tissues with cancer and inflammation may contain transcriptome variations in each cell type. Deconvolution of spatial transcriptomics profiles using variational inference (DestVI) conducts cell‐type inferences based on a continuous cell‐type model. While most spot deconvolution methods estimate the proportion of different cell types within a given spot, the results do not provide single‐cell level resolution. In contrast, cellular spatial positioning analysis via constrained expression alignment (CytoSPACE) employs single‐cell sequencing data to assign each cell to a specific spot in spatial transcriptomic data, thereby achieving single‐cell level resolution.Performing cell‐type‐based analysis is challenging when the spatial resolution of a method is lower than the size of a single cell because in this situation multiple cells can contribute to the transcripts extracted from each spot. Therefore, it is essential to estimate the proportion of cell types in each spot through deconvolution using nonspatial single‐cell sequencing data Figure . Because94 but Notably, performance benchmarking may be heavily affected by data volume, particularly in deep learning models. Additionally, the spatial resolution of each method significantly affects the performance of the cell‐type inference. For example, robust cell type decomposition (RCTD) and stereoscope use a direct‐count model for their inference; therefore, better performance is expected on high‐resolution spatial data.Detailed benchmarking of the performance of spatial and single‐cell transcriptome integration has recently been published.98 NotWhen performing cell type detection, certain cell types may not be identified if the corresponding cells or transcripts are not captured in the single‐cell sequencing data or spatial transcriptomic data. This can result in misinterpretation of the overall results, especially if the cell type of interest is rare. Therefore, to improve the reliability of a spatial transcriptomics analysis, a power analysis framework has been proposed.1004.3 The probabilistic alignment of ST experiments (PASTE) method is effective without employing histological images. Instead, PASTE finds pairwise alignment between several 2D datasets based on probable transcriptomic and spatial resemblance.Currently, NGS‐based methods may exhibit poor transcript‐capturing efficiency. The computational strategy used for improvement is to collect multiple adjacent 2D spatial transcriptome datasets from tissues and then perform alignment and integration from multiple 2D data points Figure . Here, a101 Th4.4Analyzing data produced by imaging‐based methods requires a cell segmentation task for classifying transcripts to each assigned cell Figure . Approac often resulting in inaccurate segmentation for other datasets due to the low performance of the generalized models.Manual and supervised image segmentation approaches first fluorescently stain cell bodies and/or nuclei, and then recognize cell and/or nuclei boundaries by manual or supervised algorithms. Manual approaches require huge amounts of labor and the results are often unsatisfactory. Commonly, cell boundaries are not clearly distinguishable from multiplexed FISH or ISS images. Supervised image segmentation requires a manually annotated dataset for training machine learning models,106 of Also, supervised image processing and unsupervised transcript clustering approaches can be used together to complement each other.Unsupervised approaches utilize transcript distribution to cluster transcripts for assignment to each cell. These approaches could be adequate alternatives or complements to previously discussed cell segmentation approaches because they are annotation‐free and often show better performance on cell‐level transcripts clustering as transcript distribution can be a better indicator of segmentation than visually recognizable cell boundaries in messy fluorescence images.107 Al Hence, inaccurate cell segmentation results in poor cell type identification. This issue can be resolved by employing a cell segmentation‐free method, which uses spots as a unit of cell‐type inferences. Additionally, single‐cell sequencing data can be combined to identify cell types from FISH or ISS data more precisely.By analyzing the imaging‐based spatial transcriptomics data, cell types can be also identified Figure . This ca107 He4.5Spatial data processing studies focus on single‐cell or supracellular‐scale data, probably obtained using NGS‐based methods. However, imaging‐based methods can obtain subcellular‐level details; therefore, subcellular‐level processing of the spatial distribution of transcripts will be highly important. Currently, the database may be too limited to employ deep learning approaches, as found in earlier studies by Seurat and gimVI for <2000 cells. Therefore, the performance of analysis is not guaranteed, and more experimental databases are expected to be available to develop algorithms.5FigureThe nervous system of higher organisms has a complex structure. By orchestrating organism‐level responses to internal or external stimuli, the nervous system governs complicated communications across the peripheral and central nervous systems. Because the function of the nervous system is primarily determined by the connections between functional neurons, a comprehensive understanding of this system requires elucidation of the wiring between different types of neurons by dissecting neuronal communication. Spatial transcriptomics can provide important information regarding the molecular states of cells, which can be integrated with various physiological features by precise assignment of individual cells Figure4.5.1 Spatial transcriptomic analysis on a series of coronal sections across the whole brain provides new borders for region annotation defined by molecular characteristics. The hierarchical clustering of spatial transcriptomic data points is highly correlated with conventional neuroanatomical annotation and provides novel spatial borders to segment molecular subregion candidates. To establish a single‐cell spatial atlas with cell‐type annotation, Zhang et al. profiled ≈300 000 cells in the primary motor cortex of mouse brain using MERFISH with a selected gene panel derived from the results from previous single‐cell RNA sequencing (scRNA‐seq) and single‐nucleus RNA sequencing (snRNA‐seq). Manno et al. demonstrated the potential to create a spatial atlas of developing mouse embryos by integrating scRNA‐seq data with in situ sequencing of transcripts using HybISS applying a deep‐learning‐based method. STARmap PLUS established a spatial molecular atlas with single‐cell resolution by successful segmentation from transcript annotation, from which the imputed gene expression pattern of a brain section was found to be comparable to the in situ hybridization (ISH) database of Allen Mouse Brain Atlas. STARmap PLUS also enables the study of engineered recombinant adeno‐associated virus (rAAV) tropism across whole mouse brain regions by capturing transcripts packaged in AAVs. Given that the mouse brain atlas is not merely an architectural map of the brain but also contains various functional annotations, integrating functionality with the molecular atlas is noteworthy for further improvements. The Brain Initiative Cell Census Network has initiated the integration of multiple transcriptome data generated by different methods, samples, and laboratories to construct a multimodal atlas of the primary motor cortex while tracking the laminar distribution of diverse neuronal types using MERFISH. Allen Institute for Brain Science recently announced the creation of an extensive spatial transcriptomic atlas covering the entire adult mouse brain. This achievement was made possible by integrating data from scRNA‐seq analysis of 7 million cells and MERFISH analysis of 4 million cells, resulting in a total of nearly 11 million cells analyzed. The resulting atlas is an unprecedented achievement that sheds light on the spatial organization of individual cell types and validates the role of transcription factors in determining cell type through an integrated hierarchical classification of spatial transcriptomics. Building a comprehensive reference atlas is a critical endeavor that will facilitate the generation and validation of new hypotheses as spatial transcriptomics continues to uncover novel transcriptional relationships with functions and architectures.The brain is the most segmented and annotated organ according to its spatial architecture. The brain is precisely divided into regions or areas associated with specialized functions and connectivity. However, as brain regions become more fragmented, the accuracy of defining their entities becomes controversial.111 Sp5.2scRNA‐seq has been widely employed in the molecular profiling of neurons to identify specific molecular characteristics that correlate with their physiology and function. Spatially resolved transcriptomics can provide new insights for criteria to tabulate ambiguous cell types into different categories according to their origins.The classification of neuronal types can facilitate investigating the comprehensive relationship between the structure and function of individual cells. Various criteria are applied to dissect neurons for proper classification based on their morphology, physiology, and molecular features.117scR can be analyzed through a spatially resolved transcriptomic investigation. In barcode analysis by sequencing (BARseq), the anterograde tracing virus packaged with random RNA barcodes was injected into the cortical area, and the barcodes were sequenced by in situ sequencing using BaristaSeq. The single‐cell projection pattern was identified by matching RNA barcodes and barcode reads using bulk‐RNA sequencing in multiple projection areas. The gene expression pattern from smFISH was integrated to identify the cell types of projection‐mapped neurons. BARseq2, an improved version of BARseq, allows multiplexed gene detection with padlock probes and in situ sequencing, followed by mapping of projection patterns to broad molecular profiles with single‐cell resolution.Connectivity, another criterion for neuronal classification that was merely accessible previously owing to the lack of efficient methodologies,117 ca Joglekar et al. directly observed the spatial distribution of isoforms by long‐read sequencing of RNAs captured by 10X Genomics Visium.The landscape of isoform expression is modulated by alternative splicing as post‐transcriptional regulation of gene expression, thereby contributing to the phenotype of individual cells. Therefore, different isoforms can provide various criteria for subdividing cell types identified by gene expression profiling. The conjunction of high‐throughput single‐cell gene expression profiling by 3′‐end sequencing and full‐length RNA‐seq could subdivide clusters that were previously identified by RNA profiling based on their isoform expression patterns. The gradient distribution of isoform landscape in the primary motor cortex of the mouse brain was observed using MERFISH, with marker genes correlated with isoform expression.126 Jo5.3 For instance, in vivo calcium imaging enables functional observation of neurons even within freely moving animals. Spatially resolved neuronal functionality and transcriptomics integrate different modalities of neuronal phenotypes to further dissect neuronal identities. Post‐hoc molecular profiling of the recorded cells can be carried out using IHCof ex vivo brain sections of the same animals, enabling the detection of marker genes to identify neuronal subtypes. In this regard, rather than simply detecting a few marker proteins by IHC, multiplexed FISH can further enhance the performance of molecular profiling by analyzing the higher number of markers to classify neurons of interest according to their expression patterns. Bugeon et al. developed combinatorial padlock‐probe‐amplified FISH (coppaFISH) for multiplexed FISH to detect 72 genes in the primary visual cortex of mouse brain for posthoc transcriptomic profiling after in vivo calcium imaging during visual stimulation. This increased number of detected transcripts enabled the precise classification of individual cells based on the previous scRNA‐seq transcriptome. The dependency of neuronal responses to visual stimuli according to the cortical state was related to the transcriptional profile of cells.Neural circuits are composed of substantially intermingled neuronal connections across the entire nervous system. Dissecting a specific circuit to study the functions of individual neurons is a fundamental approach to comprehending the entire system. The functional connectivity of neural circuits is being explored with developed genetic tools for dissecting neural circuits according to their phenotypic properties.129 Fo characterized the molecular profiles of neurons activated by social behavior using MERFISH. Representative marker genes for MERFISH analysis were selected after scRNA‐seq of the preoptic area, which is a subregion of the hypothalamus, and MERFISH analyzed the spatial organization of each cell type in this area. By integrating neuronal activities derived from immediate early gene expression with cellular transcriptome, the transcriptomic profile of behavior‐related neurons and their locations were identified. The analysis subdivided predefined cell types into precise classes and provided anatomical information for socially relevant neural circuits.Moffit et al.25 cha5.4 or directly characterize a new concept to find disease‐related features from spatial correlation. Chen et al. is a representative study demonstrating how to generate and validate new hypotheses concerning correlations between genetic signatures and pathological structures related to Alzheimer's disease. Novel genetic signatures for Alzheimer's disease were suggested by the identification of gene network alterations in the periphery of β‐amyloid plaques using low‐resolution spatial transcriptomics and characterization of cellular identity for cells possessing altered gene expression using ISS.Pathology of the neural system is extremely complex owing to cellular heterogeneity and complex interactions among resident cells. Spatially resolved transcriptomics offers new perspectives on how these interactions influence the study of pathological mechanisms. In mechanistic studies with animal models, spatial transcriptomics can validate specific hypotheses of causality of diseases with spatial context143 or Kaufmann et al. identified the T‐cell subtype crossing the blood‐brain barrier to colonize the brain and cause autoimmune multiple sclerosis and observed the residence of T‐cells inside the brain during disease progression using spatial transcriptomics. The same group identified putative drug targets for progressive multiple sclerosis with interactome analysis based on transcriptome distance derived from spatial transcriptomics.Studying pathology in the human brain requires precise targeting, as its physical scale generally exceeds the currently available biochemical tools used for small animals, and these procedures require pathologists who can distinguish between physiologically normal and diseased regions within a specimen. This encourages the application of spatial transcriptomics to clinical specimens to characterize the biological features that can be discriminated from a diseased partition of tissues compared to adjacent healthy tissues. Candidate genes associated with hereditary amyotrophic lateral sclerosis can be identified by detecting cerebellar granule cell layer‐specific transcripts in H&E‐stained sections of post‐mortem tissue specimens.147 Ka5.5 By integrating rich scRNA‐seq data with limited gene profiles generated by seqFISH, entire gene expression patterns were successfully inferred, and this provided additional gene profiles related to the organization of developmental processes, specifically those associated with the formation of the midbrain‐hindbrain boundary. By integration of data from spatial transcriptomics and imaging of dynamics of chromatin structure, it has been suggested that chromatin condensation is a predictive criterion for Alzheimer's disease progression. As we found in these examples, spatial transcriptomics has the potential to provide high‐dimensional molecular profiling to conventional neurodevelopmental and neuropathological investigations. Since neuroscience is a multidisciplinary field with various tools available for research, the addition of spatial information using spatial transcriptomics is expected to dramatically expand the boundaries of existing knowledge.Spatial transcriptomics has impacted various fields of neuroscience by providing additional spatial dimensions to other existing research modalities. As the application of spatial transcriptomic technology in neuroscience matures, spatially resolved transcriptome data will be further consolidated with separate analytical modalities. Applying spatial transcriptomics to the investigation of the brain during different developmental stages has enabled spatiotemporal tracking of transcriptomic changes in specific developmental structures, allowing us to impute the intermediate cellular and molecular profiles during neurodevelopment processes.150 BySpatial transcriptomics is still a new approach in neuroscience research, particularly for clinical studies. By enabling high‐throughput molecular profiling with spatial contexts, it will offer a unique opportunity to comprehend complex biological systems composed of intricate cell‐to‐cell interactions. Additionally, spatial transcriptomics holds promise as a valuable screening tool to enhance disease diagnosis accuracy, which will be further discussed in the following section.6 Spatial transcriptomics can help clinicians and scientists discover new cell types and co‐localization patterns, characterize the TIME, and monitor tumor response to therapy  and communication between cells in the TIME are associated with cancer prognosis and response to therapies.153 Sp This confirmation of spatial transcriptomics colocalization was used to support inferences on cell interaction drawn from scRNA‐seq, and subsequently, TSKs were found to be a pillar of intercellular communication. Some immune cells were also found to possibly hinder effector lymphocytes from accessing the tumor. These discoveries showcase the potential of spatial transcriptomics for discovering targets for therapeutic intervention by immune control. Overall, the cell subpopulations constituting the cSCC tumor and stroma, and interaction among their spatial niche could be characterized. Another study reinforced previous knowledge that epithelial‐to‐mesenchymal transition (EMT) and proliferation are inversely correlated—for example, an exit from the EMT is required to enter proliferation, by reporting the cellular relationship of cells of the two states. Cell states related to the respective processes were segregated into their own distinct zones, the regions being mutually exclusive. In the case of pancreatic neoplasms, spatial transcriptomics profiling directly confirmed the initial annotations of pancreatic intraepithelial neoplasia constructed with scRNA‐seq data. Furthermore, receptor‐ligand analyses suggested the interaction of the T cell immunoreceptor with Ig and ITIM domains (TIGIT) receptor of lymphocytes with Nectinligands, TIGIT being highly expressed in tumor cells. The spatial results help to weigh in on the speculation that tumor cells could exploit TIGIT‐Nectininteraction to evade the immune response.By linking spatial transcriptomes with scRNA‐seq data, new cell types can be identified, and cellular interactions can be inferred from cell‐type co‐localization patterns. In a study of human cutaneous squamous cell carcinoma (cSCC), researchers identified a specific cell type, tumor‐specific keratinocytes (TSKs), crowding the leading edge of the tumor and confirmed a surrounding fibrovascular niche consisting of cancer‐associated fibroblasts (CAFs) and endothelial cells.154 Th+, CD47+ cancer stem cell niches contributed to the remodeling of the tumor microenvironment and metastasis. Hunter et al. described an “interface” cell state located at the ecotone of tumor and normal tissues, including the possibility that this new cell state induces tumor invasion into surrounding tissues. Although the sequencing resolution and depth of spatial transcriptomic data may not be comparable to those of scRNA‐seq data, sufficient spatial data are preserved for linking and deconvoluting the spatial transcriptomic data with scRNA‐seq data. Analysis of spatial transcriptome data can indicate specific transcriptomic signals originating from the location of cellular aggregation and enables additional insights into cell types and interactions that may assist TIME formation. This can help determine whether specific cell–cell interactions are enhanced in segregated niches or separately organized structures.Spatial transcriptomics is emerging and gradually gaining recognition as an essential tool for sifting through the tumor microenvironment. A spatial transcriptome constructed from 21 tissues progressing from non‐tumor to ecotone, to tumor regions of primary lung cancer revealed that the tumor capsule was involved in transcriptome complexity, immune cell infiltrations, and continuity of intratumor spatial clusters; intratumor architecture was supported by bidirectional ligand‐receptor signaling at the cluster perimeters; and PROM1tastasis.157 HuSpatial transcriptomic data along with scRNA‐seq have become routine procedures in exploring and characterizing tissues, cell types, and diseases. Consequently, spatially resolved multi‐omics studies will be increased in cancer biology. A combination of scRNA‐seq, spatial transcriptomics, genomics, and metabolomics was used to reveal spatially segregated transcription patterns, which revealed distinct genomic alterations and identified hypoxia as a driver for genomic instability.160 Distinct clonal patterns of spatial CNAs are found near and within tumors and could be used to distinguish tumors from normal and transitional cell states. These clonal CNA patterns located in cancer drivers are not associated with immediately visible morphological transformation; therefore, they are strongly proposed as a measure for early diagnosis of cancer.The inference of copy number alterations (CNAs) using spatial transcriptomics is consistent with bulk whole‐exome sequencing (WES) data, thereby reinforcing its practicality.157 Di can infer the existence of fusion transcripts from spatial transcriptomic data. By applying this method to prostate cancer, cis‐SAGe SLC45A3‐ELK4 has been detected mostly in physiologically altered, inflamed, or neoplastic areas. Long‐read sequencing that enables the profiling of full‐length transcripts has been applied to spatial transcriptomics and scRNA‐seq. Full‐length spatial transcriptomics can be used to explore alternative splicing events during tumorigenesis.Novel transcripts, such as fusion transcripts and alternative splicing variants, are frequently observed in various cancers. Spatial transcriptomics fusion (STfusion)162 ca Spatial transcriptomics was integrated to generate maps that quantitate these genetically distinct subclones. Genetically similar subclones were found to exhibit similar co‐localization patterns and histological features regardless of their spatial vicinity, in the same way, distinct subclones localized with different groups of immune cells, possibly mediating clone‐specific immune interactions.Recently, base‐specific in situ sequencing (BaSISS) of mutation branches that were obtained from whole genome sequencing was utilized to simultaneously track the cancer‐specific somatic mutations arising in breast cancer.164 Sp The future of automating the process of histopathology‐based diagnosis is near, especially with the advent of machine learning technology and artificial intelligence. These technology‐based methods are trained on pathologist‐annotated histology slides and spatial transcriptomics data, then are able to distinguish healthy and diseased areas of the tissue, infer gene expression levels to spatially characterize tumor heterogeneity, and detect expression‐supported morphological patterns that are indistinguishable to the human eye. The application of a deep learning method trained on whole slide images to breast and lung cancer slides deduced a statistically significant link between high tumor heterogeneity and poor survival. A neural network‐based method trained to relate histological morphology to the underlying gene expressions was not only able to generally match the manual assessments by pathologists but also provided more detailed interpretation, taking the actual expression patterns into account. These techniques can detect and classify specific tumor subregions, streamlining the laborious task of manual annotation. Additionally, they can be quickly trained on large datasets and minimize potential errors and variations that may arise from human annotation. These advantages will aid in making better decisions regarding the specific treatment type for matching specific disease stages or subtypes. With the support of future research, existing histopathological diagnosis criteria may need to undergo minor to major updates to accommodate the new findings presented by spatial transcriptomics.The discovery of new factors influencing tumorigenesis and characterizing malignancy using spatial transcriptomics will bring new diagnosis criteria and standards for cancer research. As the niches of TIME, cell‐type interactions, and cancer expression regions become unveiled, they deviate from traditionally annotated regions of cancer.165 Th With more spatial cancer data acquired through movements such as the Human Tumor Atlas Network launched by the US National Cancer Institute in 2018 to construct cellular, morphological, and molecular features of progressing cancer, and with higher resolution, researchers will be more equipped to identify the key genes and regulators in the processes of mutation acquisition, factors differentiating hot and cold tumors, and tumor invasion. In summary, spatial transcriptomics technology enables the dissection of tumor heterogeneity, and its ongoing developments will reshape the cancer research landscape and open up new possibilities for precise prognosis and treatment of cancer.The use of spatial transcriptomics holds promise in cancer detection and subtype classification, however at its current state is in need of more case studies, data, and process standardization before its use as a clinical classifier.171 Wi7Here, we have provided a review of the recent technical aspects of spatial transcriptomics and related fields of application. Spatial transcriptomic technologies are rapidly evolving. This review provides historical background and practical examples in both imaging‐ and NGS‐based spatial transcriptomics.Using spatial transcriptomics is challenging and the scale of studies should be carefully considered. This sample scale is directly connected to the method of choice, and one may still struggle to use imaging‐based techniques for whole‐brain slices. Instead, gross expression patterns at a voxel of 10–20 µm are practically available on NGS‐based commercial platforms. To reduce experimental cost and improve single‐cell clustering quality, samples can be split into parts, one for non‐spatial regular single‐cell RNA‐seq and the other for the spatial version of the technique. Computational analysis is key to investigating spatial information, and established commercial platforms with modification will soon be available for ease of use.Spatial technologies are bringing new horizons in investigating epigenomes, chromosome accessibility, chromosomal structures, proteomes, CRISPR gene perturbation, lipid nanoparticles for gene therapy and mRNA vaccine development, AAV serotype optimization, and T‐cell receptor (TCR) sequencing. By combining RNA expression profiling, spatial investigation of chromosome accessibility can precisely define cell types in neuroscience and cancer research. Updated protocols have been reported for cleavage under targets and tagmentation (CUT&Tag) methods optimized for investigating specific histone mark distribution, and such methods for the spatial version are expected to be multiplexed. Epigenomic MERFISH also achieved spatial analysis of single‐cell level epigenetic features by in situ tagmentation followed by antibody‐driven capturing of epigenetic features.Recent reports have demonstrated the spatial version of assay for transposase‐accessible chromatin using sequencing (ATAC‐seq).175 By and RNA expression and protein sequencing (REAP‐seq) showed that it is possible to simultaneously analyze single‐cell transcriptome and proteome by employing DNA‐barcoded antibodies. NGS‐based spatial transcriptomics takes this a step further by incorporating antibody‐derived tags during the library generation, allowing for the analysis of spatial multi‐omics data. Spatial multiomics methods such as CITE‐seq with ST, 10X Visium, and DBiT‐seq have facilitated the sophisticated classification of cell types by integrating transcriptome and proteome data, yielding insights into the spatial organization of these molecules in tissue.Cellular indexing of transcriptomes and epitopes (CITE‐seq)180 an Genome‐wide CRISPR knockout screens to cell models have helped identify important genes in pathogenic pathways.Pooled CRISPR‐based screen with single‐cell RNA‐sequencing readouts (Perturb‐seq) has been developed by combining CRISPR screening with single‐cell sequencing methods and enables systematic investigation of the knock‐out effect of individual genes at the single‐cell level. Additionally, Perturb‐seq can explore high‐throughput intracellular and cell‐extrinsic spatial features at the single‐cell level. The recent spatial version of Perturb‐seq, Perturb‐map, utilizes combinatorial protein barcoding for a detailed spatial context of interactions between the “CRISPR‐perturbed” and surrounding cells. These toolkits will facilitate research on genomic and functional aspects of model organisms and organs with spatial molecular information on cell–cell interactions.Advances in clustered regularly interspaced short palindromic repeats (CRISPR) technology have provided versatile tools for genome editing and gene therapy.185 Ge and gene‐editing tools for delivery. Barcoded LNPs have been utilized to assess heterogeneity in cellular expression and its effect on LNP‐mediated mRNA delivery. The spatial version of this technology may open a new era for gene therapy and its efficacy assay with massive data to better understand the process of LNP formulation and mRNA inserts.Lipid nanoparticles (LNPs) are evolving for improved RNA‐based therapeutics192 an Organ and cell‐type specificities have been optimized by screening capsid proteins of AAV serotypes at single‐cell resolution. Spatial and temporal information acquired from spatial transcriptomics of optimized candidates with AAV serotype barcodes can be employed, and by reading these barcodes from each tissue and cell type, the efficiency of AAV infection can be determined in detail, which should be useful in further optimization.AAV virus serotypes have been actively optimized for target‐specific gene therapy.196 Or A recent product from 10X Genomics allows simultaneous investigation of mRNA and TCR information from H&E‐stained tissue slides. High resolution and coverage are crucial in understanding the tumor microenvironments and metastases; therefore, the sensitivity and coverage of sequences need further improvement.Spatial transcriptomics and TCR sequencing are essential for tracing and dissecting T‐cell infiltration and their interactions in metastatic cancer tissues.199 A  NanoString Technologies reported the compatibility of their CosMx Spatial Molecular Imager (SMI) platform with FFPE samples. Based on these commercial platforms, spatial transcriptomics can be more easily applied in clinical research.For clinical applications, spatial transcriptomics should be effective in clinical practice and sampling environments. Nucleic acids in human tissue specimens are readily degraded prior to cryopreservation. Certain clinical institutions, such as university hospitals, have limited resources for deep freezing to preserve nucleic acids from nuclease digestion. Moreover, histological diagnosis and research are based on FFPEsamples; therefore, next‐generation technologies should be effective in analyzing heavily fixed samples. 10X Genomics has recently announced two platforms for NGS‐based and imaging‐based spatial transcriptomes for FFPE samples.201 NaFinally, spatial information obtained from clinical samples should yield new classes of biomarkers that are often underestimated because of the lack of appropriate investigative technologies. Therefore, seamlessly integrating multiple sources of transcriptome data that may or may not include spatial or molecular information is important. Computational efforts to analyze massive spatial transcriptomic data may help improve the success of clinical applications for marker discovery and drug development.The authors declare no conflict of interest.S.H.J. and R.H.L. contributed equally to this work. H.‐E.P. and C.H.S. conceptualized the contributions of all technologies. H.‐E.P. and C.H.S. wrote the part for imaging and NGS‐based methods, application notes in neuroscience, and future directions and conclusions. S.H.J. and T.K. wrote the computational analysis part. R.H.L. and J.P. wrote the application notes for cancer studies. C.P.M., C.W.L., and J.K.H. reviewed and revised the manuscript and provided critical comments.


Felis catus) were domesticated thousands of years ago, but despite artificial selection for a wide variety of coat colors, the wild‐type pattern of tabby cats is very common. We aimed to determine whether this pattern grants an advantage over other morphs in natural environments. We collected cat images taken with camera traps in natural areas near and far from 38 rural settlements in Israel, to compare the habitat use by feral cats of different colors. We tested the effect of proximity to villages and habitat vegetation  on the probability of space use by the tabby morph compared to the others. NDVI had a positive effect on site use in both morphs, but non‐tabby cats had a 2.1 higher probability of using the near sites than the far sites, independent of NDVI. The wild‐type tabby cats' probability of site use were equally likely to be unaffected by proximity, or have an interaction of proximity with NDVI whereby the far transects are used with increasing probability in sites of denser vegetation. We hypothesize that the camouflage of tabby cats, more than other colors and patterns, confers an advantage in roaming the woodland habitats for which this pattern evolved. This has both theoretical implications as rare empirical evidence of the adaptive value of fur coloration, and practical implications on managing the ecological impact of feral cats worldwide.Camouflage is a common trait enabling animals to avoid detection by predators and prey. Patterns such as spots and stripes are convergent across carnivore families, including felids, and are hypothesized to have adaptive value through camouflage. House cats ( We collected house cat images taken with camera traps in natural areas near and far from villages, to compare the habitat use by feral cats of different pelage colors. The space use by cats with non‐wild‐type coloration is lower in the far cameras compared to the near ones, whereas wild‐type tabby appearance does not change with proximity. We ttribute this to the tabbies' camouflage in the woodland region, as supported by a positive correlation of vegetation and far site usage. Previous studies on cat coloration suggested that coat patterns are likely to be adaptive for camouflage rather than communication or physiological reasons  using the “check_overdispersion” function in the R package “performance”  deployed as part of Israel's National Terrestrial Biodiversity Monitoring Program run by Hamaarag—Israel's National Nature Assessment Program , as well as an interaction model of proximity and NDVI  and wildcats ; formal analysis (lead); investigation (lead); visualization (lead); writing – original draft ; writing – review and editing . Itai Namir: Data curation ; formal analysis (supporting); investigation (supporting); writing – review and editing (supporting). Ron Chen: Data curation (supporting); project administration ; resources (lead); writing – review and editing (supporting). Eran Levin: Conceptualization (lead); project administration ; supervision (lead); writing – original draft ; writing – review and editing .This study was partially funded by the Azrieli Graduate Studies Fellowship.The authors declare no conflict of interest.


Improved understanding of causal risk factors for child and adolescent mental health problems are dependent on observational research. Although confounding is a major limitation of observational research, this problem is widely ignored in the reporting and dissemination of findings from observational studies in psychiatric journals. There is clearly a need for improved reporting of confounding and more careful interpretation of observational research in psychiatry. As randomized clinical trials, the gold standard approach for addressing confounding and establishing causal effects, are unfeasible or unethical for most putative risk factors, advances in the causal understanding of risk factors for child and adolescent mental health problems are dependent on observational research  works to improve the reliability and value of publications of research through transparent and complete reporting. The EQUATOR Network have developed reporting guidelines for all of the main study designs in health research, such as the Consolidated Standards of Reporting Trials (CONSORT) statement, the Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) statement and the Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis (TRIPOD)  statement.The internationally recognised EQUATOR Network  is another major reporting guideline that has been developed by the EQUATOR Network for the reporting of observational research. Because bias due to confounding is a major limitation of observational research, the STROBE checklist includes multiple items related to confounding .Recent research have examined the reporting of confounding in observational studies (Munkholm et al., Two papers in the June issue of JCPP Advances represent useful examples of how observational studies can explore theory‐driven, biologically plausible hypothesis about risk factors and help identify the mechanisms through which risk factors are associated with child and adolescent mental health problems. The publication by Walle et al. , in the The study by Fish‐Williamson et al.  providesIn contrast to many publications in psychiatric specialty journals (Munkholm et al., Reports of research should provide clarity around which questions were addressed and why, what was done, what was shown, and what the findings mean. Even though reporting quality of a study does not necessarily reflect the methodological quality of the study, transparent and complete reporting is needed to allow readers to evaluate the strengths and limitations of the study and to replicate the study and also to conduct evidence‐synthesis studies. Insufficient reporting, therefore, hampers possibilities to effectively translate research into improved care for young people with mental health problems.Most readers of JCPP Advances probably agree that the identification and discussion of the limitations of a study are a critical aspect of reporting. Although confounding is a major limitation of observational research it is widely ignored in the reporting and dissemination of findings from observational studies in psychiatric journals (Munkholm et al., Henrik Larsson: Conceptualization; Writing – original draft; Writing – review & editing.Advances.Henrik Larsson reports receiving grants from Shire Pharmaceuticals; personal fees from and serving as a speaker for Medice, Shire/Takeda Pharmaceuticals and Evolan Pharma AB outside the submitted work; and sponsorship for a conference on attention‐deficit/hyperactivity disorder from Shire Pharmaceuticals outside the submitted work. He is the Editor‐in‐Chief for JCPP


While refinements in existing therapies have led to small incremental improvement in lung cancer outcomes over the years, nothing has had quite the same impact as the development of immunotherapy, specifically checkpoint inhibitors. Immune checkpoint inhibitors first made headlines in the modern era following the 2010 publishing of a randomized controlled trial showing a significant survival benefit of ipilimumab in metastatic melanoma.The tumour microenvironment (TME) describes the interplay between tumour cells, infiltrating and adjacent immune cells, stromal cells, cellular components and non‐cell factors including extra‐cellular matrix (ECM), cytokines and chemokinesSecretion of chemokines by tumour cells lead to recruitment of specific cell types and differentiation into cells conducive to immune evasion and tumour progression.Tumour‐infiltrating lymphocytes and macrophages are also seconded to promotion of tumour survival. DCs express several immunomodulatory molecules and in to responding to them, also secrete immune suppressing cytokines including IL‐10. This immunosuppressive milieu, along with down regulation of cell attachment molecules on tumour associated vessels,The importance of the TME has been shown in analysis of lung cancer samples. In a study examining the characteristics of lung cancer TME by whole exome and RNA‐seq analysis, Shinohara and colleagues classified tumours according to their degree of tumour proliferation, immunosuppression and anti‐tumour immunity.Immune checkpoints act to maintain homeostasis, avoid run away inflammation, and sustain self‐tolerance.Another potent checkpoint dimer in the B7‐CD28/CTLA‐4 superfamily is programmed death (PD)‐1 and its ligands, programmed death ligand (PD‐L)1 and PD‐L2, homologues of B7. As its names suggests it was initially thought to be responsible for apoptosis but was later found to be integral in immune regulation.Both anti‐CLTA‐4 and anti‐PD‐1/anti‐PD‐L1 were tested across numerous animal studies of malignancy with variable results. In a mouse model of lung cancer, a homologue of ipilimumab blocking CTLA‐4 was ineffective as monotherapy but highly effective in combination with chemotherapy.The mechanism of immune‐related adverse effects is poorly understood. Genetic loss of function polymorphisms of CTLA‐4 has been studied and an associated auto‐immune phenotype described,While use of checkpoint inhibitors in routine clinical practice is relatively recent, the science behind them is relatively ‘old’. This commentary has highlighted the value of extensive basic science of T cell immunobiology and how it has yielded insights that combined with preclinical cancer models provided an exceptional example of the power of translational science to design novel cancer treatments.