Tissue Labeling Research Articles

TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine.

Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized. Use of reduced vector to represent transcriptome data independent of platforms. Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples. Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness. TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .

Double-layered two-directional somatopleural cell migration during chicken body wall development revealed with local fluorescent tissue labeling.

The thoracic ventral body wall consists of the rib, the sternum, the intercostal muscles, and the connective tissues surrounding them. The ribs and the intercostal muscles are derived from the somite. The connective tissues are derived from the somatic layer of the lateral plate mesoderm, somatopleure. The lateral growth of the somatopleure forms the primary ventral body wall. The migration of somitic cells into the somatopleure generates the secondary body wall. As the migrating behavior of the somatopleural cells during secondary body wall formation is still unclear, we investigate here the migratory behavior of the somatopleural cells in the thorax during chicken ventral body wall development by labeling the thoracic somatopleural cells one-somite-wide by DiI labeling or gene transfection of the enhanced green fluorescent protein and observe their distribution assisted by the tissue-clearing technique FRUIT. Our labeling experiments revealed the rostral migration of the somatopleural cells into a deep part of the thoracic body wall in embryonic day 6.5 chickens. For embryonic day 8.5 chickens, these deep-migrating somatopleural cells were found around the sternal ribs. Thus, we identified the double-layered two-directional migrating pathways of the somatopleural cells: the rostral migration of the deep somatopleural cells and the lateral migration of the superficial somatopleural cells. Our findings imply that the rostral migration of deep somatopleural cells and the lateral migration of superficial ones might be associated with the developing sternal ribs and the innervation of the thoracic cutaneous nerves, respectively.

Longitudinal Intravital Microscopy Using a Mammary Imaging Window with Replaceable Lid

The branched structure of the mammary gland is highly dynamic and undergoes several phases of growth and remodeling after birth. Intravital microscopy in combination with skin flap surgery or implantation of imaging windows has been used to study the dynamics of the healthy mammary gland at different developmental stages. Most mammary imaging technologies are limited to a time frame of hours to days, whereas the majority of mammary gland remodeling processes occur in time frames of days to weeks. To study mammary gland remodeling, methods that allow optical access to the tissue of interest for extended time frames are required. Here, an improved version of the titanium mammary imaging window with a replaceable lid (R.MIW) is described that allows high-resolution imaging of the mammary gland with a cellular resolution for up to several weeks. Importantly, the R.MIW provides tissue access over the entire duration of the intravital imaging experiment and could therefore be used for local tissue manipulation, labeling, drug administration, or image-guided microdissection. Taken together, the R.MIW enables high-resolution characterization of the cellular dynamics during mammary gland development, homeostasis, and disease.

Longitudinal Intravital Microscopy Using a Mammary Imaging Window with Replaceable Lid.

The branched structure of the mammary gland is highly dynamic and undergoes several phases of growth and remodeling after birth. Intravital microscopy in combination with skin flap surgery or implantation of imaging windows has been used to study the dynamics of the healthy mammary gland at different developmental stages. Most mammary imaging technologies are limited to a time frame of hours to days, whereas the majority of mammary gland remodeling processes occur in time frames of days to weeks. To study mammary gland remodeling, methods that allow optical access to the tissue of interest for extended time frames are required. Here, an improved version of the titanium mammary imaging window with a replaceable lid (R.MIW) is described that allows high-resolution imaging of the mammary gland with a cellular resolution for up to several weeks. Importantly, the R.MIW provides tissue access over the entire duration of the intravital imaging experiment and could therefore be used for local tissue manipulation, labeling, drug administration, or image-guided microdissection. Taken together, the R.MIW enables high-resolution characterization of the cellular dynamics during mammary gland development, homeostasis, and disease.

Investigation and Classification of Chronic Wound Tissue images Using Random Forest Algorithm (RF)

The broad increase make use of digital cameras, by hand wound imaging has turn out to be common practice in experimental place. There is in malice of still a condition for a reasonable device for accurate wound curing consideration between dimensional facility and tissue categorization in a exacting simple to exploit technique We achieved the major unit of this plan by computing a 3-D model for wound dimensions using un calibrated revelation techniques. We highlight at this point on tissue classification from color and eminence region descriptors computed after unverified segmentation. As a result of perception distortions, unconstrained lighting provisions and viewpoints, wound assessments modify commonly in the middle of patient review. The majority significant separation of this article is to overcome this trouble by means of a multi inspection approach for tissue classification, relying on a 3-D model onto which tissue labels are mapped and categorization result merged. The investigational categorization tests communicate that improved repeatability and robustness are obtained and that metric assessment is attain through appropriate region and degree dimensions and wound chart origin. In this manuscript we proposed wound image segmentation, tissue classification in grouping with the Random Forest (RF). These methodology are helpful for classifying the rate of injured tissue in a segmented element and improved accuracy.

Standard techniques for formalin-fixed paraffin-embedded tissue: A Pathologist’s perspective

Histopathology considered as an essential keystone to understanding diseases on a cellular level, without examining affected tissues and cells; will lack the accurate diagnosis. The most common protocol that used worldwide for histopathology is termed as formalin-fixed paraffin-embedded techniques; this protocol is the oldest protocol and still used, this technique not invented once, but it is started with a simple process of tissue fixation then undergo several successful steps as it's known today. This protocol depends on formalin to induce tissue fixation and preservation simultaneously, followed by a series of steps to facilitate infiltration of hot paraffin wax in the cells to adjust the density of tissue similar to that of paraffin wax to support tissue during sectioning process at few micrometers of thickness. Many protocols have been invented and modified subsequently to increase the accuracy of tissue processing and facilitate their study under light microscopy. Additionally, the formalin-fixed and paraffin-embedded samples can be considered the most durable process till now. In this review, we are trying to view all fundamental aspects of classical tissue processing that was depended since 1850s, starting from tissue examination, sampling, accessory, labelling, fixation, dehydration, clearance, infiltration, and embedding with hot paraffin wax. Additionally, explain the routine sectioning and staining with classical Hari’s hematoxylin and alcoholic eosin staining protocol.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling

Accurate segmentation of the brain into gray matter, white matter, and cerebrospinal fluid using magnetic resonance (MR) imaging is critical for visualization and quantification of brain anatomy. Compared to 3T MR images, 7T MR images exhibit higher tissue contrast that is contributive to accurate tissue delineation for training segmentation models. In this paper, we propose a cascaded nested network (CaNes-Net) for segmentation of 3T brain MR images, trained by tissue labels delineated from the corresponding 7T images. We first train a nested network (Nes-Net) for a rough segmentation. The second Nes-Net uses tissue-specific geodesic distance maps as contextual information to refine the segmentation. This process is iterated to build CaNes-Net with a cascade of Nes-Net modules to gradually refine the segmentation. To alleviate the misalignment between 3T and corresponding 7T MR images, we incorporate a correlation coefficient map to allow well-aligned voxels to play a more important role in supervising the training process. We compared CaNes-Net with SPM and FSL tools, as well as four deep learning models on 18 adult subjects and the ADNI dataset. Our results indicate that CaNes-Net reduces segmentation errors caused by the misalignment and improves segmentation accuracy substantially over the competing methods.

Bias-invariant RNA-sequencing metadata annotation.

BackgroundRecent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs.FindingsHere, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning–based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression–based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm can learn biases from datasets with few samples.ConclusionUsing our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable.

Accelerated clearing and molecular labeling of biological tissues using magnetohydrodynamic force

Techniques used to clear biological tissue for fluorescence microscopy are essential to connect anatomical principles at levels ranging from subcellular to the whole animal. Here we report a simple and straightforward approach to efficiently render opaque tissue samples transparent and show that this approach can be modified to rapidly label intact tissue samples with antibodies for large volume fluorescence microscopy. This strategy applies a magnetohydrodynamic (MHD) force to accelerate the removal of lipids from tissue samples at least as large as an intact adult mouse brain. We also show that MHD force can be used to accelerate antibody penetration into tissue samples. This strategy complements a growing array of tools that enable high-resolution 3-dimensional anatomical analyses in intact tissues using fluorescence microscopy. MHD-accelerated clearing is simple, fast, reliable, inexpensive, provides good thermal regulation, and is compatible with existing strategies for high-quality fluorescence microscopy of intact tissues.

Local islet remodelling associated with duct lesion-islet complex in adult human pancreas.

Islets are thought to be stably present in the adult human pancreas to maintain glucose homeostasis. However, identification of the pancreatic intraepithelial neoplasia (PanIN)-islet complex in mice and the presence of PanIN lesions in adult humans suggest that similar remodelling of islet structure and environment may occur in the human pancreas. To identify islet remodelling in a clinically related setting, we examine human donor pancreases with 3D histology to detect and characterise the human PanIN-islet complex. Cadaveric donor pancreases (26-65years old, n = 10) were fixed and sectioned (350μm) for tissue labelling, clearing and microscopy to detect local islet remodelling for 3D analysis of the microenvironment. The remodelled microenvironment was subsequently examined via microtome-based histology for clinical assessment. In nine pancreases, we identified the unique peri-lobular islet aggregation associated with the PanIN lesion (16 lesion-islet complexes detected; size: 3.18 ± 1.34mm). Important features of the lesion-islet microenvironment include: (1) formation of intra-islet ducts, (2) acinar atrophy, (3) adipocyte association, (4) inflammation (CD45+), (5) stromal accumulation (α-SMA+), (6) increase in Ki-67 proliferation index but absence of Ki-67+ alpha/beta cells and (7) in-depth and continuous duct-islet cell contacts, forming a cluster. The duct-islet cell cluster and intra-islet ducts suggest likely islet cell neogenesis but not replication. We identify local islet remodelling associated with PanIN-islet complex in the adult human pancreas. The tissue remodelling and the evidence of inflammation and stromal accumulation suggest that the PanIN-islet complex is derived from tissue repair after a local injury.

Abstract 2544: TGF-β receptors 1 and 2 are functional biomarkers that stratify risk of hepatocellular cancer (HCC). Artificial intelligence based validation at three centers

Abstract Background: Hepatocellular carcinoma (HCC) is the fastest rising cancer in the USA and is the fourth leading cause of cancer deaths globally. A critical unmet need to reduce high mortality associated with advanced HCC is the ability to identify high-risk individuals for cost-effective, targeted screening, early detection, and prevention strategies. Human genomics and animal models have revealed etiological patterns and multiple genes and signaling pathways such as TGF-β, WNT, VEGF to be associated with the initiation and progression of HCC. We used integrated functional approaches (combining bioinformatics analysis, in vivo mouse models, and in vitro biological and biochemical methods) and identified potential biomarkers for HCC. We examined and validated 10 functional biomarkers for HCC risk prediction. We further harnessed machine learning tools and Artificial Intelligence (AI)-based technology to validate manual analyses of Immunohistochemical (IHC) labeled slides across different centers. Methods: >280 liver tissue samples were collected from patients with HCC and cirrhosis. Immunohistochemistry was performed against 10 functional biomarkers (TGFBR1, TGFBR2, SPTBN1, HMGA2, RSPO3, ITIH4, EPCAM, PLK1, SIRT6, FANCD2). Artificial intelligence-based deep learning technology by AIFORIA was used for the unbiased, intensive, and rapid validation of IHC. Results: The most promising results were for TGFBR1 and TGFBR2, on >80 HCC and cirrhotic samples, IHC labeling revealed: • Both TGFBR1 (p<0.001) and TGFBR2 (p<0.02) showed considerably reduced expression levels in tumor tissue within HCC samples, compared to non-tumorous adjacent tissue and cirrhotic samples. • The results were validated with another set of 70 patient samples at two other centers. Results obtained at George Washington University (GW), University of Maryland (UMD), and the University of Hawaii (UH) for TGFBR2 were consistent. For TGFBR1, GW and UMD showed reproducible results while results at UH lacked statistical significance. • AI-based analysis for TGFBR2 (p<0.002) validated the results from manual analysis ensuring the consistency of results across different sites. • Of the other biomarkers, PLK1 also displayed potentially substantial alterations with increased labeling in tumor tissue as compared to non-tumor tissue. Conclusions: TGFBR2 is a promising functional predictive HCC tissue biomarker. • Tissue biomarkers are important as they provide spatial and contextual information. • Computerized analysis is a powerful tool to capture microscopic patterns in visual data that are not normally assessed in manual analysis such as the pattern of immune cells in cirrhosis and HCC. • AI adds consistency and confidence to results obtained at different sites. Citation Format: Sobia Zaidi, Kirty Shetty, Herbert Yu, Linda Wong, Shuyun Rao, Wilma Jogunoori, Richard Amdur, Shulin Li, Patricia latham, Bao-Ngoc Nguyen, Lopa Mishra. TGF-β receptors 1 and 2 are functional biomarkers that stratify risk of hepatocellular cancer (HCC). Artificial intelligence based validation at three centers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2544.

A Nature-Inspired Metal-Organic Framework Discriminator for Differential Diagnosis of Cancer Cell Subtypes.

Metabolic glycan labeling (MGL) followed by bioorthogonal chemistry provides a powerful tool for tumor imaging and therapy. However, selectively metabolic labeling of cells or tissues of interest remains a challenge. Particularly, owing to tumor heterogeneity including tumor subtypes and interpatient heterogeneity, it is far more difficult to realize tumor-cell-selective metabolic labeling for precise diagnosis. Inspired by nature, we designed azidosugar-functionalized metal-organic frameworks camouflaged with cancer cell membranes to accomplish cancer-cell-selective MGL in vivo. With abundant receptors, this biomimetic platform not only selectively targets homotypic cells but also realizes different breast cancer subtype-selective MGL. Moreover, the endo/lysosomal-escaped ZIF-8 can make azidosugar escape from lysosomes and accelerate its metabolic incorporation. This strategy also takes advantage of cancer-tissue-derived cell membranes, which may have huge potential for personalized diagnosis and therapy.

A Nature‐Inspired Metal–Organic Framework Discriminator for Differential Diagnosis of Cancer Cell Subtypes

AbstractMetabolic glycan labeling (MGL) followed by bioorthogonal chemistry provides a powerful tool for tumor imaging and therapy. However, selectively metabolic labeling of cells or tissues of interest remains a challenge. Particularly, owing to tumor heterogeneity including tumor subtypes and interpatient heterogeneity, it is far more difficult to realize tumor‐cell‐selective metabolic labeling for precise diagnosis. Inspired by nature, we designed azidosugar‐functionalized metal–organic frameworks camouflaged with cancer cell membranes to accomplish cancer‐cell‐selective MGL in vivo. With abundant receptors, this biomimetic platform not only selectively targets homotypic cells but also realizes different breast cancer subtype‐selective MGL. Moreover, the endo/lysosomal‐escaped ZIF‐8 can make azidosugar escape from lysosomes and accelerate its metabolic incorporation. This strategy also takes advantage of cancer‐tissue‐derived cell membranes, which may have huge potential for personalized diagnosis and therapy.

Bone Preparation from Embalmed Human Cadavers - A Retrieval and Curation Technique

Bone preparation involves soft tissue removal, maceration, bleaching and labelling. In the absence of a standardized methodology a large repository of human bones are lost, as most medical colleges do not process bones after the dissection of human cadavers. The present study therefore conducted with the aim of evaluating the least time-consuming and effective method of bone preparation from embalmed and wet specimens. The method used included a process of maceration, which involves soft tissue removal and then boiling the bones in 60 litres of water for 2 hours. The process of maceration was augmented by adding potassium hydroxide pellets (caustic potash mol. wt. 56.11) after 30 minutes of initiation of boiling; 200-250 gm in the case of male bones and 150 – 200 gm in the case of female bones. After maceration was complete, the bones were bleached by soaking them in 30 – 35 litres of hydrogen peroxide 30% w/v solution (mol. wt. 34.01) for 12-14 hours. The bleached bones were then washed with water and soaked in 30 -35 litres of acetone (extra pure mol. wt. 58.08, boiling point 55.5° – 56°C) for 12 hours to degrease them. The bones dried naturally by spreading them on blotting paper and subsequently painted with a mixture of half a litre of lacquer and half a litre of lacquer thinner. This study concluded that the preparation of bones using the above method was effective, fast, odourless, and good quality human bones for anatomical study resulted.

A nomogram combining PPARγ expression profiles and clinical factors predicts survival in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with poor prognosis. Peroxisome proliferator-activated receptor γ (PPARγ) is involved in the development of various tumor types. However, its role in hepatocellular carcinoma (HCC) remains unclear. Multiple databases including The Cancer Genome Atlas, Gene Expression Omnibus and Kaplan-Meier plotter were used for bioinformatics analysis of the PPARγ gene or protein. Immunohistochemical labeling of tumor and adjacent normal tissues obtained from 125 patients with HCC was performed to analyze the relationship between PPARγ expression and overall survival (OS) rate. PPARγ was evaluated using functional enrichment analyses and Lasso regression was used to conduct a dimensionality reduction analysis of 43 clinical factors for HCC. An OS prognostic nomogram was then established using seven independent risk factors screened via Lasso regression. PPARγ expression in HCC tumor tissues was higher compared with that in normal liver tissues, and its high expression was associated with poor prognosis, as indicated by bioinformatics analysis. However, opposite results were obtained using the clinical specimens. Functional enrichment analysis indicated that PPARγ was enriched in the ‘fatty acid metabolism’ pathway. Lasso regression identified seven clinical factors associated with prognosis, including Tumor-Node-Metastasis stage, grade, vascular invasion, α fetoprotein, carbohydrate antigen 199, γ-glutamyl transpeptidase and the PPARγ protein. These seven clinical factors were to construct an OS prognostic nomogram. Overall, PPARγ was highly expressed in the livers of patients with HCC and can be included in an OS prognostic nomogram. However, the factors underlying the differential association of PPARγ expression with HCC prognosis in different datasets should be further investigated.

Identification of proteins and cellular pathways targeted by 2-nitroimidazole hypoxic cytotoxins

Tumour hypoxia negatively impacts therapy outcomes and continues to be a major unsolved clinical problem. Nitroimidazoles are hypoxia selective compounds that become entrapped in hypoxic cells by forming drug-protein adducts. They are widely used as hypoxia diagnostics and have also shown promise as hypoxia-directed therapeutics. However, little is known about the protein targets of nitroimidazoles and the resulting effects of their modification on cancer cells. Here, we report the synthesis and applications of azidoazomycin arabinofuranoside (N3-AZA), a novel click-chemistry compatible 2-nitroimidazole, designed to facilitate (a) the LC-MS/MS-based proteomic analysis of 2-nitroimidazole targeted proteins in FaDu head and neck cancer cells, and (b) rapid and efficient labelling of hypoxic cells and tissues. Bioinformatic analysis revealed that many of the 62 target proteins we identified participate in key canonical pathways including glycolysis and HIF1A signaling that play critical roles in the cellular response to hypoxia. Critical cellular proteins such as the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the detoxification enzyme glutathione S-transferase P (GSTP1) appeared as top hits, and N3-AZA adduct formation significantly reduced their enzymatic activities only under hypoxia. Therefore, GAPDH, GSTP1 and other proteins reported here may represent candidate targets to further enhance the potential for nitroimidazole-based cancer therapeutics.

In Vivo Three-Photon Imaging of Lipids using Ultrabright Fluorogens with Aggregation-Induced Emission.

Fluorescent probes capable of in vivo lipids labeling are highly desirable for studying lipid-accumulation-related metabolic diseases, such as nonalcoholic fatty liver disease, type-2 diabetes, and atherosclerosis. However, most of the current lipid-specific fluorophores cannot be used for in vivo labeling due to their strong hydrophobicity. Herein, organic dots from bright luminogens with aggregation-induced emission (AIEgen) are developed for in vivo labeling and three-photon fluorescence imaging of lipid-rich tissues, such as fatty liver, atherosclerotic plaques in brain vasculatures, and carotid arteries. The organic dots show excellent stability in an aqueous medium with high targeting specificity to lipids and strong three-photon fluorescence in the far-red/near-infrared (NIR) region under NIR-II laser excitation, which enables efficient in vivo labeling and imaging of lipids in deep tissues. The study will inspire the development of lipid-targeting fluorophores for in vivo applications.

Optimization and evaluation of fluorescence in situ hybridization chain reaction in cleared fresh-frozen brain tissues.

Transcript labeling in intact tissues using in situ hybridization chain reaction has potential to provide vital spatiotemporal information for molecular characterization of heterogeneous neuronal populations. However, large tissue labeling in non-perfused or fresh-frozen rodent and postmortem human samples, which provide more flexible utilization than perfused tissues, is largely unexplored. In the present study, we optimized the combination of in situ hybridization chain reaction in fresh-frozen rodent brains and then evaluated the uniformity of neuronal labeling between two clearing methods, CLARITY and iDISCO+. We found that CLARITY yielded higher signal-to-noise ratios but more limited imaging depth and required longer clearing times, whereas, iDISCO+ resulted in better tissue clearing, greater imaging depth and a more uniform labeling of larger samples. Based on these results, we used iDISCO+-cleared fresh-frozen rodent brains to further validate this combination and map the expression of a few genes of interest pertaining to mood disorders. We then examined the potential of in situ hybridization chain reaction to label transcripts in cleared postmortem human brain tissues. The combination failed to produce adequate mRNA labeling in postmortem human cortical slices but produced visually adequate labeling in the cerebellum tissues. We next, investigated the multiplexing ability of in situ hybridization chain reaction in cleared tissues which revealed inconsistent fluorescence output depending upon the fluorophore conjugated to the hairpins. Finally, we applied our optimized protocol to assess the effect of glucocorticoid receptor overexpression on basal somatostatin expression in the mouse cortex. The constitutive glucocorticoid receptor overexpression resulted in lower number density of somatostatin-expressing neurons compared to wild type. Overall, the combination of in situ hybridization chain reaction with clearing methods, especially iDISCO+, may find broad application in the transcript analysis in rodent studies, but its limited use in postmortem human tissues can be improved by further optimizations.

Ion-dependent slow protein release from in vivo disintegrating micro-granules

Through the controlled addition of divalent cations, polyhistidine-tagged proteins can be clustered in form of chemically pure and mechanically stable micron-scale particles. Under physiological conditions, these materials act as self-disintegrating protein depots for the progressive release of the forming polypeptide, with potential applications in protein drug delivery, diagnosis, or theragnosis. Here we have explored the in vivo disintegration pattern of a set of such depots, upon subcutaneous administration in mice. These microparticles were fabricated with cationic forms of either Zn, Ca, Mg, or Mn, which abound in the mammalian body. By using a CXCR4-targeted fluorescent protein as a reporter building block we categorized those cations regarding their ability to persist in the administration site and to sustain a slow release of functional protein. Ca2+ and specially Zn2+ have been observed as particularly good promoters of time-prolonged protein leakage. The released polypeptides result is available for selective molecular interactions, such as specific fluorescent labeling of tumor tissues, in which the protein reaches nearly steady levels.