Cellular and molecular bioengineering最新文献

Introduction: Coaxial 3D bioprinting has advanced the formation of tissue constructs that recapitulate key architectures and biophysical parameters for in-vitro disease modeling and tissue-engineered therapies. Controlling gene expression within these structures is critical for modulating cell signaling and probing cell behavior. However, current transfection strategies are limited in spatiotemporal control because dense 3D scaffolds hinder diffusion of traditional vectors. To address this, we developed a coaxial extrusion 3D bioprinting technique using ultrasound-responsive gene delivery bioinks. These bioink materials incorporate echogenic microbubble gene delivery particles that upon ultrasound exposure can sonoporate cells within the construct, facilitating controllable transfection.

Methods: Phospholipid-coated gas-core microbubbles were electrostatically coupled to reporter transgene plasmid payloads and incorporated into cell-laden alginate bioinks at varying particle concentrations. These bioinks were loaded into the coaxial nozzle core for extrusion bioprinting with CaCl₂ crosslinker in the outer sheath. Resulting bioprints were exposed to 2.25 MHz focused ultrasound and evaluated for microbubble activation and subsequent DNA delivery and transgene expression.

Results: Coaxial printing parameters were established that preserved the stability of ultrasound-responsive gene delivery particles for at least 48 h in bioprinted alginate filaments while maintaining high cell viability. Successful sonoporation of embedded cells resulted in DNA delivery and robust ultrasound-controlled transgene expression. The number of transfected cells was modulated by varying the number of focused ultrasound pulses applied. The size region over which DNA was delivered was modulated by varying the concentration of microbubbles in the printed filaments.

Conclusions: Our results present a successful coaxial 3D bioprinting technique designed to facilitate ultrasound-controlled gene delivery. This platform enables remote, spatiotemporally-defined genetic manipulation in coaxially bioprinted tissue constructs with important applications for disease modeling and regenerative medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00818-x.

Purpose: In many diseases, an overabundance of macrophages contributes to adverse outcomes. While numerous studies have compared macrophage phenotype after mechanical stimulation or with varying local stiffness, it is unclear if and how macrophages directly contribute to mechanical forces in their microenvironment.

Methods: Raw 264.7 murine macrophages were embedded in a confining agarose gel, and proliferated to form spheroids over days/weeks. Gels were synthesized at various concentrations to tune stiffness and were shown to support cell viability and spheroid growth. These cell-agarose constructs were treated with media supplements to promote macrophage polarization. Spheroid geometries were used to computationally model the strain generated in the agarose by macrophage spheroid growth. Agarose-embedded macrophages were analyzed for viability, spheroid size, stress generation, and gene expression.

Results: Macrophages form spheroids and generate growth-induced mechanical forces (i.e., solid stress) within confining agarose gels, which can be maintained for at least 16 days in culture. Increasing agarose concentration increases gel stiffness, restricts spheroid expansion, limits gel deformation, and causes a decrease in Ki67 expression. Lipopolysaccharide (LPS) stimulation increases spheroid growth, though this effect is reversed with the addition of IFNγ. The mechanosensitive ion channels Piezo1 and TRPV4 have reduced expression with increased stiffness, externally applied compression, LPS stimulation, and M1-like polarization.

Conclusions: Macrophages alone both respond to and generate solid stress. Understanding how macrophage generation of growth-induced solid stress responds to different environmental conditions will help to inform treatment strategies for the plethora of diseases that involve macrophage accumulation and inflammation.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00824-z.

Introduction: Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells.

Methods: Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts.

Results: Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity.

Conclusions: Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00823-0.

Purpose: The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity.

Methods: Our approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a ResNet-50 binary classification model, identifying which Z-planes contained invaded cells and measuring the total invasion depth. Lastly, microvessel dynamics were assessed through a U-Net Xception-style segmentation model for vessel prediction, the DisPerSE algorithm to extract an embedded graph, then graph analysis to quantify microvessel length and connectivity. To further validate our software, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Lastly, we applied this software to two naive image datasets from coculture lumen and microvascular fragment models.

Results: The software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC₅₀ values with a 95% confidence level compared to manual calculations. This approach significantly reduced the image processing time from weeks down to h. Furthermore, the software was able to calculate cell coverage, microvessel length, and invasion depth from two additional microphysiological models that were imaged with confocal microscopy, highlighting the versatility of the software.

Conclusions: Our free and open source software offers an automated solution for quantifying 3D cell behavior in microphysiological models assessed using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.This software can be found in our GitHub repository: https://github.com/fogg-lab/tissue-model-analysis-tools.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00821-2.

Purpose: Current bulk molecular assays fail to capture spatial signaling activities in cancers, limiting our understanding of drug resistance mechanisms. We developed a graph-based super-resolution protein-protein interaction (GSR-PPI) technique to spatially resolve single-cell signaling networks and evaluate whether higher resolution microscopy enhances the biological study of PPIs using deep learning classification models.

Methods: Single-cell spatial proximity ligation assays (PLA, ≤ 9 PPI pairs) were conducted on EGFR mutant (EGFRm) PC9 and HCC827 cells (>10,000 cells) treated with 100 nM Osimertinib. Multiplexed PPI images were obtained using wide-field and super-resolution microscopy (Zeiss Airyscan, SRRF). Graph-based deep learning models analyzed subcellular protein interactions to classify drug treatment states and test GSR-PPI on clinical tissue samples. GSR-PPI triangulated PPI nodes into 3D relationships, predicting drug treatment labels. Biological discriminative ability (BDA) was evaluated using accuracy, AUC, and F1 scores. The method was also applied to 3D spatial proteomic molecular pixelation (PixelGen) data from T cells.

Results: GSR-PPI outperformed baseline models in predicting drug responses from multiplexed PPI imaging in EGFRm cells. Super-resolution data significantly improved accuracy over localized wide-field imaging. GSR-PPI classified drug treatment states in cancer cells and human lung tissues, with performance improving as imaging resolution increased. It differentiated single and combination drug therapies in HCC827 cells and human tissues. Additionally, GSR-PPI accurately distinguished T-cell stimulation states, identifying key nodes such as CD44, CD45, and CD54.

Conclusion: The GSR-PPI framework provides valuable insights into spatial protein interactions and drug responses, enhancing the study of signaling biology and drug resistance.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00822-1.

Objectives

This review explores the potential applications of large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4 in the medical field, aiming to encourage their prudent use, provide professional support, and develop accessible medical AI tools that adhere to healthcare standards.

Methods

This paper examines the impact of technologies such as OpenAI's Generative Pre-trained Transformers (GPT) series, including GPT-3.5 and GPT-4, and other large language models (LLMs) in medical education, scientific research, clinical practice, and nursing. Specifically, it includes supporting curriculum design, acting as personalized learning assistants, creating standardized simulated patient scenarios in education; assisting with writing papers, data analysis, and optimizing experimental designs in scientific research; aiding in medical imaging analysis, decision-making, patient education, and communication in clinical practice; and reducing repetitive tasks, promoting personalized care and self-care, providing psychological support, and enhancing management efficiency in nursing.

Results

LLMs, including ChatGPT, have demonstrated significant potential and effectiveness in the aforementioned areas, yet their deployment in healthcare settings is fraught with ethical complexities, potential lack of empathy, and risks of biased responses.

Conclusion

Despite these challenges, significant medical advancements can be expected through the proper use of LLMs and appropriate policy guidance. Future research should focus on overcoming these barriers to ensure the effective and ethical application of LLMs in the medical field.

Introduction

The majority of ovarian cancer (OC) patients receiving standard of care chemotherapy develop chemoresistance within 5 years. The tumor microenvironment (TME) is a dynamic and influential player in disease progression and therapeutic response. However, there is a lack of models that allow us to elucidate the compartmentalized nature of TME in a controllable, yet physiologically relevant manner and its critical role in modulating drug resistance.

Methods

We developed a 3D microvascularized multiniche tumor-on-a-chip formed by five chambers (central cancer chamber, flanked by two lateral stromal chambers and two external circulation chambers) to recapitulate OC-TME compartmentalization and study its influence on drug resistance. Stromal chambers included endothelial cells alone or cocultured with normal fibroblasts or cancer-associated fibroblasts (CAF).

Results

The tumor-on-a-chip recapitulated spatial TME compartmentalization including vessel-like structure, stromal-mediated extracellular matrix (ECM) remodeling, generation of oxygen gradients, and delayed drug diffusion/penetration from the circulation chamber towards the cancer chamber. The cancer chamber mimicked metastasis-like migration and increased drug resistance to carboplatin/paclitaxel treatment in the presence of CAF when compared to normal fibroblasts. CAF-mediated drug resistance was rescued by ECM targeted therapy. Critically, these results demonstrate that cellular crosstalk recreation and spatial organization through compartmentalization are essential to determining the effect of the compartmentalized OC-TME on drug resistance.

Conclusions

Our results present a functionally characterized microvascularized multiniche tumor-on-a-chip able to recapitulate TME compartmentalization influencing drug resistance. This technology holds the potential to guide the design of more effective and targeted therapeutic strategies to overcome chemoresistance in OC.

Introduction

Bacterial surface display is a valuable biotechnology technique for presenting proteins and molecules on the outer surface of bacterial cells. However, it has limitations, including potential toxicity to host bacteria and variability in display efficiency. To address these issues, we investigated the removal of abundant non-essential outer membrane proteins (OMPs) in E. coli as a new strategy to improve the surface display of recombinant proteins.

Methods

We targeted OmpA, a highly prevalent OMP in E. coli, using the lambda red method. We successfully knocked out ompA in two E. coli strains, K-12 MG1655 and E. coli BL-21, which have broad research and therapeutic applications. We then combined ompA knockout strains and two OMPs with three therapeutic proteins including an anti-toxin enzyme (ClbS), interleukin 18 (IL-18) for activating cytotoxic T cells and an anti- CTLA4 nanobody (αCTLA4) for immune checkpoint blockade.

Results

A total of six different display constructs were tested for their display levels by flow cytometry, showing that the ompA knockout strains increased the percentage as well as the levels of display in bacteria compared to those of isogenic wild-type strains.

Conclusions

By removing non-essential, highly abundant surface proteins, we develop an efficient platform for displaying enzymes and antibodies, with potential industrial and therapeutic applications. Additionally, the enhanced therapeutic efficacy opens possibilities for live bacteria-based therapeutics, expanding the technology’s relevance in the field.

Purpose

Affibodies are a class of versatile affinity proteins with a wide variety of therapeutic applications, ranging from contrast agents for imaging to cell-targeting therapeutics. We have identified several affibodies specific to bone morphogenetic protein-2 (BMP-2) with a range of binding affinities and demonstrated the ability to tune release rate of BMP-2 from affibody-conjugated poly(ethylene glycol) maleimide (PEG-mal) hydrogels based on affibody affinity strength. In this work, we compare the purity, structure, and activity of recombinant, bacterially-expressed BMP-2-specific affibodies with affibodies synthesized via solid-phase peptide synthesis.

Methods

High- and low-affinity BMP-2-specific affibodies were recombinantly expressed using BL21(DE3) E. coli and chemically synthesized using microwave-assisted solid-phase peptide synthesis with Fmoc-Gly-Wang resin. The secondary structures of the affibodies and dissociation constants of affibody-BMP-2 binding were characterized by circular dichroism and biolayer interferometry, respectively. Endotoxin levels were measured using chromogenic limulus amebocyte lysate (LAL) assays. Affibody-conjugated PEG-mal hydrogels were fabricated and loaded with BMP-2 to evaluate hydrogel capacity for controlled release, quantified by enzyme-linked immunosorbent assays (ELISA).

Results

Synthetic and recombinant affibodies were determined to be α-helical by circular dichroism. The synthetic high- and low-affinity BMP-2-specific affibodies demonstrated comparable BMP-2 binding dissociation constants to their recombinant counterparts. Recombinant affibodies retained some endotoxins after purification, while endotoxins were not detected in the synthetic affibodies above FDA permissible limits. High-affinity affibody-conjugated hydrogels reduced cumulative BMP-2 release compared to the low-affinity affibody-conjugated hydrogels and hydrogels without affibodies.

Conclusions

Synthetic affibodies demonstrate comparable structure and function to recombinant affibodies while reducing endotoxin contamination and increasing product yield, indicating that solid-phase peptide synthesis is a viable method of producing affibodies for controlled protein release and other applications.