Non-Specific Binding, a Limitation of the Immunofluorescence Method to Study Macrophages In Situ

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Spectral fingerprinting has emerged as a powerful tool, adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Through the use of Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages as compared to M2 and naïve phenotypes. NIR fluorescence data also indicate that distinctive intra-endosomal environments of these cell types give rise to significant differences in many optical features such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of > 95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications such as cell phenotyping or mapping intra-endosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT 6 give rise to more improved model accuracy (> 87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

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Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications

Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50-75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.

A bicellular fluorescent ductal carcinoma in situ (DCIS)-like tumoroid to study the progression of carcinoma: practical approaches and optimization

Recently, many types of 3D culture systems have been developed to preserve the physicochemical environment and biological characteristics of the original tumors better than the conventional 2D monolayer culture system. There are various types of models belonging to this culture, such as the culture based on non-adherent and/or scaffold-free matrices to form the tumors. Agarose mold has been widely used to facilitate tissue spheroid assembly, as it is essentially non-biodegradable, bio-inert, biocompatible, low-cost, and low-attachment material that can promote cell spheroidization. As no studies have been carried out on the development of a fluorescent bicellular tumoroid mimicking ductal carcinoma in situ (DCIS) using human cell lines, our objective was to detail the practical approaches developed to generate this model, consisting of a continuous layer of myoepithelial cells (MECs) around a previously formed in situ breast tumor. The practical approaches developed to generate a bi-cellular tumoroid mimicking ductal carcinoma in situ (DCIS), consisting of a continuous layer of myoepithelial cells (MECs) around a previously formed in situ breast tumoroid. Firstly, the optimal steps and conditions of spheroids generation using a non-adherent agarose gel were described, in particular, the appropriate medium, seeding density of each cell type and incubation period. Next, a lentiviral transduction approach to achieve stable fluorescent protein expression (integrative system) was used to characterize the different cell lines and to track tumoroid generation through immunofluorescence, the organization of the two cell types was validated, specific merits and drawbacks were compared to lentiviral transduction. Two lentiviral vectors expressing either EGFP (Enhanced Green Fluorescent Protein) or m-Cherry (Red Fluorescent Protein) were used. Various rates of a multiplicity of infection (MOI) and multiple types of antibodies (anti-p63, anti-CK8, anti-Maspin, anti-Calponin) for immunofluorescence analysis were tested to determine the optimal conditions for each cell line. At MOI 40 (GFP) and MOI 5 (m-Cherry), the signals were almost homogeneously distributed in the cells which could then be used to generate the DCIS-like tumoroids. Images of the tumoroids in agarose molds were captured with a confocal microscope Micro Zeiss Cell Observer Spinning Disk or with IncuCyte® to follow the progress of the generation. Measurement of protumoral cytokines such as IL-6, IL8 and leptin confirmed their secretion in the supernatants, indicating that the properties of our cells were not altered. Finally the advantages and disadvantages of each fluorescent approach were discussed. This model could also be used for other solid malignancies to study the complex relationship between different cells such as tumor and myoepithelial cells in various microenvironments (inflammatory, adipose and tumor, obesity, etc.). Although, this new model is well established to monitor drug screening applications and perform pharmacokinetic and pharmacodynamic analyses.

In Situ Gene Expression in Native Cryofixed Bone Tissue

Bone is a very complex tissue that is constantly changing throughout the lifespan. The precise mechanism of bone regeneration remains poorly understood. Large bone defects can be caused by gunshot injury, trauma, accidents, congenital anomalies and tissue resection due to cancer. Therefore, understanding bone homeostasis and regeneration has considerable clinical and scientific importance in the development of bone therapy. Macrophages are well known innate immune cells secreting different combinations of cytokines and their role in bone regeneration during bone healing is essential. Here, we present a method to identify mRNA transcripts in cryosections of non-decalcified rat bone using in situ hybridization and hybridization chain reaction to explore gene expression in situ for better understanding the gene expression of the bone tissues.

Macrophage Identification In Situ

Understanding the processes of inflammation and tissue regeneration after injury is of great importance. For a long time, macrophages have been known to play a central role during different stages of inflammation and tissue regeneration. However, the molecular and cellular mechanisms by which they exert their effects are as yet mostly unknown. While in vitro macrophages have been characterized, recent progress in macrophage biology studies revealed that macrophages in vivo exhibited distinctive features. Actually, the precise characterization of the macrophages in vivo is essential to develop new healing treatments and can be approached via in situ analyses. Nowadays, the characterization of macrophages in situ has improved significantly using antigen surface markers and cytokine secretion identification resulting in specific patterns. This review aims for a comprehensive overview of different tools used for in situ macrophage identification, reporter genes, immunolabeling and in situ hybridization, discussing their advantages and limitations.