Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.
Nanoparticles (NP) interact with complex protein milieus in biological fluids, and these interactions have profound effects on NP physicochemical properties and function. Surprisingly, most studies neglect the impact of these interactions, especially with respect to NP-mediated siRNA delivery. Here, the effects of serum on colloidal stability and siRNA delivery of a pH-responsive micellar NP delivery system were characterized. Results show cationic NP-siRNA complexes aggregate in ≥ 2% serum in buffer, but are stable in serum-free media. Furthermore, non-aggregated NP-siRNA delivered in serum-free media result in 4-fold greater siRNA uptake in vitro, compared to aggregated NP-siRNA. Interestingly, pH-responsive membrane lysis behavior, which is required for endosomal escape, and NP-siRNA dissociation, necessary for mRNA knockdown, are significantly reduced in serum. Consistent with these data, non-aggregated NP-siRNA in serum-free conditions result in highly efficient gene silencing, even at doses as low as 5 nM siRNA. NP-siRNA diameter was measured at albumin and IgG levels mimicking biological fluids. Neither albumin nor IgG alone induces NP-siRNA aggregation, implicating other serum proteins in NP colloidal instability. Finally, as a proof-of-principle that stability is maintained in established in vivo models, transmission electron microscopy reveals NP-siRNA are taken up by ductal epithelial cells in a non-aggregated state when injected retroductally into mouse salivary glands in vivo. Overall, this study shows serum-induced NP-siRNA aggregation significantly diminishes efficiency of siRNA delivery by reducing uptake, pH-responsive membrane lysis activity, and NP-siRNA dissociation. Moreover, these results highlight the importance of local NP-mediated drug delivery, and are broadly applicable to other drug delivery systems.
RNA interference has immense potential to modulate cell functions. However, effective delivery of small interfering RNA (siRNA) while avoiding deleterious side effects has proven challenging. This study investigates both intended and unintended effects of diblock copolymer nanoparticle (NP) delivery of siRNA delivery to human mesenchymal stem cells (hMSC). Specifically, siRNA delivery was investigated at a range of NP‐siRNA:hMSC ratios with a focus on the effects of NP‐siRNA treatment on hMSC functions. Additionally, next generation RNA sequencing (RNAseq) was used with enrichment analysis to observe side effects in hMSC gene expression. Results show NP‐siRNA delivery is negatively correlated with hMSC density. However, higher NP‐siRNA:hMSC ratios increased cytotoxicity and decreased metabolic activity. hMSC proliferation was largely unaffected by NP‐siRNA treatment, except for a threefold reduction in hMSCs seeded at 4,000 cells/cm2. Flow cytometry reveals that apoptosis is a function of NP‐siRNA treatment time and seeding density; ∼14% of the treated hMSCs seeded at 8,000 cells/cm2 were annexin V+‐siRNA+ 24 hr after treatment, while 11% of the treated population was annexin V+‐siRNA−. RNAseq shows that NP‐siRNA treatment results in transcriptomic changes in hMSCs, while pathway analysis shows upregulation of apoptosis signaling and downregulation of metabolism, cell cycle, and DNA replication pathways, as corroborated by apoptosis, metabolism, and proliferation assays. Additionally, multiple innate immune signaling pathways such as toll‐like receptor, RIG‐I‐like receptor, and nuclear factor‐κB signaling pathways are upregulated. Furthermore, and consistent with traditional siRNA immune activation, cytokine–cytokine receptor signaling was also upregulated. Overall, this study provides insight into NP‐siRNA:hMSC ratios that are favorable for siRNA delivery. Moreover, NP‐siRNA delivery results in side effects across the hMSC transcriptome that suggest activation of the innate immunity that could alter MSC functions associated with their therapeutic potential.
Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.
Programmed cell death, or apoptosis, is an essential process in development and homeostasis, and disruptions in associated pathways are responsible for a wide variety of diseases such as cancer, developmental abnormalities, and Alzheimer's disease. On the other hand, cell death, in many cases, is the desired outcome of therapeutic treatments targeting diseases such as cancer. Recently, metabolic imaging based on two-photon fluorescence microscopy has been developed and shown to be highly sensitive to certain cell death processes, most notably apoptosis, thus having the potential as an advanced label-free screening tool. However, the typically low acquisition rates of this imaging technique have resulted in a limited throughput approach, allowing only a small population of cells to be tracked at well-separated time points. To address this limitation, a high-speed two-photon fluorescence lifetime imaging microscopy (2P-FLIM) platform capable of video-rate imaging is applied to study and further characterize the metabolic dynamics associated with cell death. Building upon previous work demonstrating the capabilities of this system, this microscope is utilized to study rapid metabolic changes during cell death induction, such as dose-dependency of metabolic response, response in invasive vs. noninvasive cancer cells, and response in an apoptosis-resistant cell line, which is further shown to undergo autophagy in response to toxic stimuli. Results from these experiments show that the early apoptosis-related metabolic dynamics are strongly correlated with important cellular parameters including responsiveness to apoptosis-inducing stimuli. The high speed and sensitivity of the presented imaging approach enables new investigations into this highly dynamic and complex process.
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