Level Of Individual Cells Research Articles

A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing.

Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.

Comparison of FDA-Approved vs. Mayo Clinic’s Ki-67 Immunostaining Protocols for Breast Cancer Specimens: A Quantitative Analysis Using Artificial Intelligence (AI) Based Image Analysis Algorithms

Abstract Introduction/Objective Ki-67, a nuclear protein present during the active phases of the cell cycle, is an important prognostic and predictive marker for many tumor types, including breast cancer. This study compared the results of breast cancer specimens immunohistochemically (IHC) stained with the FDA-approved vs. Mayo Clinic’s (MC) validated Ki-67 protocol using AI-based image analysis algorithms for quantitative examination at an individual cell level. Methods/Case Report Sixty-two breast cancer specimens were selected with a rough equal representation of Ki-67 percentage in low (<10%), intermediate (10-20%) and high (>20%) categories. Two adjacent sections of each specimen were stained with the FDA and MC protocols, respectively, and scanned to obtain whole slide digital images. Two AI algorithms, one with tumor cell identification and the other without, were used to quantify positive and negative cells and calculate Ki-67 percentages. Results (if a Case Study enter NA) Both algorithms show high concordance between the two staining protocols, with r-squared scores of 0.989 and 0.992, respectively, analyzing with or without tumor identification. AI identified tumor better on MC stained slides vs. FDA stained slides. The results demonstrate a high-performance concordance between the MC and the FDA IHC staining protocols. Conclusion The data also illustrates the accuracy of using AI image analysis algorithms to compare different IHC protocols at an individual cell level. The AI tumor identification difference in slides stained with two IHC protocols reflects the importance of training an algorithm with a specific staining protocol to achieve optimal tumor recognition.

Does prey availability influence the detection of Dinophysis spp. by the imaging FlowCytobot?

The Imaging FlowCytobot (IFCB) is a field-deployable imaging-in-flow cytometer that is increasingly being used to monitor harmful algae. The IFCB acquires images of suspended particles based on their chlorophyll-a fluorescence and/or the amount of light they scatter (side scattering). The present study hypothesized that fluorescence-based image acquisition would undercount Dinophysis spp., a genus of non-constitutive mixotrophs, when prey is limited. This is because Dinophysis spp. acquire plastids via ingestion of their ciliate prey Mesodinium spp., and lose photosynthetic capacity and autofluorescence in the absence of prey. Even small blooms of Dinophysis spp. can be highly toxic and result in diarrhetic shellfish poisoning (DSP), highlighting the importance of accurately detecting low abundances. To explore this, laboratory experiments were conducted to determine optimal IFCB settings for a fed culture of Dinophysis acuminata, and an existing time series of IFCB observations collected in Puget Sound (Washington, U.S.A) was used to compare Dinophysis spp. abundance estimates from samples triggered via side scattering versus fluorescence in relation to Mesodinium spp. abundance. This study introduces a quantitative approach for optimizing the detection of target harmful algae which can be repeated across multiple IFCBs and demonstrates the effects of IFCB calibration on Dinophysis spp. detection. The laboratory experiments showed that IFCB settings for fluorescence-based image acquisition need to be fairly sensitive to accurately detect D. acuminata cells. A poorly calibrated IFCB can miss a significant proportion of D. acuminata abundance whatever the method used to trigger the image acquisition. Field results demonstrated that the physiological status of Dinophysis spp. can influence their detection by the IFCB when triggering on fluorescence. This was observed during a 7-day period when the IFCB failed to detect Dinophysis spp. cells when triggering on fluorescence while cells were still detected using the side scattering triggering method as well as observed by microscopy. During this period, Mesodinium spp. was not detected, IFCB-derived autofluorescence level of individual cells of Dinophysis spp. was low, and less than 50 % of Dinophysis spp. cells exhibited autofluorescence under the microscope. Together, this indicates that the unique feeding ecology of Dinophysis spp. may affect their detection by the IFCB when cells are starved.

Comparison of Live Cell Enrichment Methods for Single-Cell Rnaseq

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular diversity and gene expression profiles at the individual cell level. However, to ensure accurate analysis of scRNA-seq data, it is crucial to generate high-quality single-cell suspensions by effectively removing dead cells, debris, and ambient RNA. This study aims to assess and compare the performance of five commercially available methods for dead cell removal: Fluorescence activated cell sorting (FACS), Akadeum Microbubble kit, StemCell EasySep kit, Miltenyi Biotec Dead Cell Removal Kit, and LevitasBio Levicell Live Cell Enrichment. Among the 5 methods we evaluated, FACS is considered the gold standard technique due to its remarkable efficiency and versatility. However, the high-pressure nature of FACS can exert mechanical stress on cells during the sorting process, potentially affecting cellular viability and gene expression profiles. On the other hand, the remaining four methods, while less versatile, utilize milder conditions, which may better preserve fragile cell populations. To evaluate those 5 methods, we used two tissues requiring only no to mild dissociation process: lymph node and cerebrospinal fluid (CSF). Viability of cells from each tissue was assessed using trypan blue staining after dissociation. The cell suspensions were then subjected to the five dead cell removal methods following the manufacturer's instructions. For each cell suspension, 10,000 cells were targeted using the 10x Genomics 3' v3.1 kit. The evaluation criteria encompassed hands-on time, required equipment, cost, improvement in live cell percentage, cell type recovery, gene expression profile, and cellular stress response. We hope these findings will provide valuable guidance for researchers in selecting the most suitable dead cell removal method based on their experimental requirements, thereby facilitating efficient, cost-effective, and high-quality scRNA-seq analysis.

Towards Ultraviolet Microbeam Scanning and Lens-Less UV Microbeam Microscopy with Mirror Galvanometric Scanners: From the History of Research Instrumentation to Engineering of Modern Mechatronic Optical Systems

This article aims to ensure continuity between classical methods of ul-traviolet microscopy or/and micromanipulation using ultraviolet mi-crobeam and lens-less ultraviolet microscopy and microbeam exposure of cells and tissues. Considering the history of the development of the method and the possibility of working with different methods of mechanical scanning, the authors propose to use mirror galvanometers and an electromechanical scanning system in the mechanical engineering of lensless microbeam installations. These installations make it possible to provide both scanning with an ultraviolet microbeam to obtain a line scan image, and precision micromanipulation at the level of individual cells or individual organelles (in the case of large cells). We propose to extrapolate mathematical models previously developed for galvanic mirrors of light-beam oscilloscopes for microbeam scanning systems, position-sensitive micromanipulation, and real-time microphotometric cell analysis.

Influence of synthetic and natural microfibers on the growth, substance exchange, energy accumulation, and oxidative stress of field-collected microalgae compared with microplastic fragment

Synthetic microfibers (MFs), which are Microplastics (MPs), have not received attention commensurate with their abundance in the environment. Currently, limited studies on MFs have focused on their effects on marine organisms. It is therefore necessary to conduct exposure experiments of MFs on freshwater organisms to provide reference data for the ecological risk assessment of MFs. As a primary producer in freshwater ecosystems, microalgae have an ecological niche that is highly overlapping with that of MFs. In this study, we examined the effects of MFs on the growth of Chlorella and indicators of oxidative stress to examine their potential risk on the microalgae population. The results showed that inhibition rate of microalgae increased with MF concentration in the range of 0.01–100 mg/L. Compared with natural fibers such as cotton and wool, PET and PP fibers showed significant growth inhibition, but less so when in fragment form with the same material and concentration. PP and PET particles, whether fibers or fragments, increased the total antioxidant capacity of microalgal cells and caused oxidative damage. To determine the influence of MFs on the interaction of cells in the environment, the exchanged substances and accumulated energy of microalgae cells were also detected. The results indicated that PP and PET fibers, as well as fragments, increased the diameter and membrane permeability of microalgae cell, thus interfering with the cell division and substance exchange processes. PET fibers and fragments showed different interactions at the level of individual cells and populations. This suggests that the evaluation of MPs should consider examinations from cells to population and even community levels in the future.

Integrated single-cell and bulk RNA sequencing analysis identified pyroptosis-related signature for diagnosis and prognosis in osteoarthritis

Osteoarthritis (OA), a degenerative disease of the joints, has one of the highest disability rates worldwide. This study investigates the role of pyroptosis-related genes in osteoarthritis and their expression in different chondrocyte subtypes at the individual cell level. Using OA-related datasets for single-cell RNA sequencing and RNA-seq, the study identified PRDEGs and DEGs and conducted Cox regression analysis to identify independent prognostic factors for OA. CASP6, NOD1, and PYCARD were found to be prognostic factors. Combined Weighted Gene Correlation Network Analysis with PPI network, a total of 15 hub genes related to pyroptosis were involved in the notch and oxidative phosphorylation pathways, which could serve as biomarkers for the diagnosis and prognosis of OA patients. The study also explored the heterogeneity of chondrocytes between OA and normal samples, identifying 19 single-cell subpopulation marker genes that were significantly different among 7 chondrocyte cell clusters. AGT, CTSD, CYBC, and THYS1 were expressed differentially among different cell subpopulations, which were associated with cartilage development and metabolism. These findings provide valuable insights into the molecular mechanisms underlying OA and could facilitate the development of new therapeutic strategies for this debilitating disease.

The developmental hierarchy and scarcity of replicative slender trypanosomes in blood challenges their role in infection maintenance

The development of Trypanosoma brucei in its mammalian host is marked by a distinct morphological change as replicative "slender" forms differentiate into cell cycle arrested "stumpy" forms in a quorum-sensing-dependent manner. Although stumpy forms dominate chronic infections at the population level, the proportion of replicative parasites at the individual cell level and the irreversibility of arrest in the bloodstream are unclear. Here, we experimentally demonstrate that developmental cell cycle arrest is definitively irreversible in acute and chronic infections in mice. Furthermore, analysis of replicative capacity and single-cell transcriptome profiling reveal a temporal hierarchy, whereby cell cycle arrest and appearance of a reversible stumpy-like transcriptome precede irreversible commitment and morphological change. Unexpectedly, we show that proliferating parasites are exceptionally scarce in the blood after infections are established. This challenges the ability of bloodstream trypanosomes to sustain infection by proliferation or antigenic variation, these parasites instead being overwhelmingly adapted for transmission.

Validation of an on-chip p16ink4a/Ki-67 dual immunostaining cervical cytology system using microfluidic device technology

More specific screening systems for cervical cancer may become necessary as the human papillomavirus (HPV) vaccine becomes more widespread. Although p16/Ki-67 dual-staining cytology has several advantages, it requires advanced diagnostic skills. Here, we developed an automated on-chip immunostaining method using a microfluidic device. An electroactive microwell array (EMA) microfluidic device with patterned thin-film electrodes at the bottom of each microwell was used for single-cell capture by dielectrophoresis. Immunostaining and dual staining for p16/Ki-67 were performed on diagnosed liquid cytology samples using the EMA device. The numbers of p16/Ki-67 dual-stained cells captured by the EMA device were determined and compared among the cervical intraepithelial neoplasia (CIN) lesion samples. Seven normal, fifteen CIN grade 3, and seven CIN grade 2 samples were examined. The percentage of dual-positive cells was 18.6% in the CIN grade 2 samples and 23.6% in the CIN grade 3 samples. The percentages of dual-positive staining increased significantly as the severity of the cervical lesions increased. p16/Ki67 dual immunostaining using the EMA device is as sensitive as the conventional method of confirming the histopathological diagnosis of cervical samples. This system enables a quantified parallel analysis at the individual cell level.

Single-cell RNA-sequencing data clustering using variational graph attention auto-encoder with self-supervised leaning.

The emergence of single-cell RNA-seq (scRNA-seq) technology makes it possible to capture their differences at the cellular level, which contributes to studying cell heterogeneity. By extracting, amplifying and sequencing the genome at the individual cell level, scRNA-seq can be used to identify unknown or rare cell types as well as genes differentially expressed in specific cell types under different conditions using clustering for downstream analysis of scRNA-seq. Many clustering algorithms have been developed with much progress. However, scRNA-seq often appears with characteristics of high dimensions, sparsity and even the case of dropout events', which make the performance of scRNA-seq data clustering unsatisfactory. To circumvent the problem, a new deep learning framework, termed variational graph attention auto-encoder (VGAAE), is constructed for scRNA-seq data clustering. In the proposed VGAAE, a multi-head attention mechanism is introduced to learn more robust low-dimensional representations for the original scRNA-seq data and then self-supervised learning is also recommended to refine the clusters, whose number can be automatically determined using Jaccard index. Experiments have been conducted on different datasets and results show that VGAAE outperforms some other state-of-the-art clustering methods.

Potential value of high-throughput single-cell DNA sequencing of Juvenile myelomonocytic leukemia: report of two cases

Juvenile myelomonocytic leukemia (JMML) is a rare myeloproliferative disease of early childhood that develops due to mutations in the genes of the RAS-signaling pathway. Next-generation high throughput sequencing (NGS) enables identification of various secondary molecular genetic events that can facilitate JMML progression and transformation into secondary acute myeloid leukemia (sAML). The methods of single-cell DNA sequencing (scDNA-seq) enable overcoming limitations of bulk NGS and exploring genetic heterogeneity at the level of individual cells, which can help in a better understanding of the mechanisms leading to JMML progression and provide an opportunity to evaluate the response of leukemia to therapy. In the present work, we applied a two-step droplet microfluidics approach to detect DNA alterations among thousands of single cells and to analyze clonal dynamics in two JMML patients with sAML transformation before and after hematopoietic stem cell transplantation (HSCT). At the time of diagnosis both of our patients harbored only “canonical” mutations in the RAS signaling pathway genes detected by targeted DNA sequencing. Analysis of samples from the time of transformation JMML to sAML revealed additional genetic events that are potential drivers for disease progression in both patients. ScDNA-seq was able to measure of chimerism level and detect a residual tumor clone in the second patient after HSCT (sensitivity of less than 0.1% tumor cells). The data obtained demonstrate the value of scDNA-seq to assess the clonal evolution of JMML to sAML, response to therapy and engraftment monitoring.

Unveiling novel insights in prostate cancer through single-cell RNA sequencing.

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technology that provides insights at the individual cell level. In contrast to traditional bulk RNA-seq, which captures gene expression at an average level and may overlook important details, scRNA-seq examines each individual cell as a fundamental unit and is particularly well-suited for identifying rare cell populations. Analogous to a microscope that distinguishes various cell types within a tissue sample, scRNA-seq unravels the heterogeneity and diversity within a single cell species, offering great potential as a leading sequencing method in the future. In the context of prostate cancer (PCa), a disease characterized by significant heterogeneity and multiple stages of progression, scRNA-seq emerges as a powerful tool for uncovering its intricate secrets.

Advancement of single-cell sequencing for clinical diagnosis and treatment of pancreatic cancer.

Single-cell sequencing is a high-throughput technique that enables detection of genomic, transcriptomic, and epigenomic information at the individual cell level, offering significant advantages in detecting cellular heterogeneity, precise cell classification, and identifying rare subpopulations. The technique holds tremendous potential in improving the diagnosis and treatment of pancreatic cancer. Moreover, single-cell sequencing provides unique insights into the mechanisms of pancreatic cancer metastasis and cachexia, paving the way for developing novel preventive strategies. Overall, single-cell sequencing has immense potential in promoting early diagnosis, guiding personalized treatment, and preventing complications of pancreatic cancer. Emerging single-cell sequencing technologies will undoubtedly enhance our understanding of the complex biology of pancreatic cancer and pave the way for new directions in its clinical diagnosis and treatment.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research.

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

The Contribution of Multiplexing Single Cell RNA Sequencing in Acute Myeloid Leukemia.

Decades ago, the treatment for acute myeloid leukemia relied on cytarabine and anthracycline. However, advancements in medical research have introduced targeted therapies, initially employing monoclonal antibodies such as ant-CD52 and anti-CD123, and subsequently utilizing specific inhibitors that target molecular mutations like anti-IDH1, IDH2, or FLT3. The challenge lies in determining the role of these therapeutic options, considering the inherent tumor heterogeneity associated with leukemia diagnosis and the clonal drift that this type of tumor can undergo. Targeted drugs necessitate an examination of various therapeutic targets at the individual cell level rather than assessing the entire population. It is crucial to differentiate between the prognostic value and therapeutic potential of a specific molecular target, depending on whether it is found in a terminally differentiated cell with limited proliferative potential or a stem cell with robust capabilities for both proliferation and self-renewal. However, this cell-by-cell analysis is accompanied by several challenges. Firstly, the scientific aspect poses difficulties in comparing different single cell analysis experiments despite efforts to standardize the results through various techniques. Secondly, there are practical obstacles as each individual cell experiment incurs significant financial costs and consumes a substantial amount of time. A viable solution lies in the ability to process multiple samples simultaneously, which is a distinctive feature of the cell hashing technique. In this study, we demonstrate the applicability of the cell hashing technique for analyzing acute myeloid leukemia cells. By comparing it to standard single cell analysis, we establish a strong correlation in various parameters such as quality control, gene expression, and the analysis of leukemic blast markers in patients. Consequently, this technique holds the potential to become an integral part of the biological assessment of acute myeloid leukemia, contributing to the personalized and optimized management of the disease, particularly in the context of employing targeted therapies.

Analysis of Fatty Acid Metabolism in Fetal and Failing Hearts by Single-Cell RNA Sequencing Revealed SLC27A6 as a Critical Gene in Heart Maturation.

Heart failure is associated with shifts in substrate preferences and energy insufficiency. Although cardiac metabolism has been explored at the organ level, the metabolic changes at the individual cell level remain unclear. This study employed single-cell ribonucleic acid (RNA) sequencing to investigate the cell-type-specific characteristics of gene expression related to fatty acid metabolism. Single-cell RNA sequencing data from fetal hearts were processed to analyze gene expression patterns related to fatty acid metabolism. Immunofluorescence staining and Western blotting techniques were employed to validate the expression of specific proteins. Additionally, calcium recording and contractility measurements were performed to assess the functional implications of fatty acid metabolism in cardiomyocytes. Based on single-cell RNA sequencing data analysis, we found that a decrease in overall energy requirements underlies the downregulation of fatty acid oxidation-related genes in the later period of heart maturation and the compensatory increase of fatty acid metabolism in individual cardiomyocytes during heart failure. Furthermore, we found that solute carrier family 27 member 6 (SLC27A6), a fatty acid transport protein, is involved in cardiac maturation. SLC27A6 knockdown in human induced pluripotent stem cell-derived cardiomyocytes resulted in an immature cardiomyocyte transcriptional profile, abnormal morphology, impaired Ca2+ handling activity, and contractility. Overall, our study offers a novel perspective for exploring cardiac fatty acid metabolism in fetal and failing hearts along with new insights into the cellular mechanism underlying fatty acid metabolic alterations in individual cardiac cells. It thus facilitates further exploration of cardiac physiology and pathology.

Single-cell transcriptome sequencing reveals heterogeneity of gastric cancer: progress and prospects.

Gastric cancer is one of the most serious malignant tumor and threatens the health of people worldwide. Its heterogeneity leaves many clinical problems unsolved. To treat it effectively, we need to explore its heterogeneity. Single-cell transcriptome sequencing, or single-cell RNA sequencing (scRNA-seq), reveals the complex biological composition and molecular characteristics of gastric cancer at the level of individual cells, which provides a new perspective for understanding the heterogeneity of gastric cancer. In this review, we first introduce the current procedure of scRNA-seq, and discuss the advantages and limitations of scRNA-seq. We then elaborate on the research carried out with scRNA-seq in gastric cancer in recent years, and describe how it reveals cell heterogeneity, the tumor microenvironment, oncogenesis and metastasis, as well as drug response in to gastric cancer, to facilitate early diagnosis, individualized therapy, and prognosis evaluation.

C1M2: a universal algorithm for 3D instance segmentation, annotation, and quantification of irregular cells.

Cell instance segmentation is a fundamental task for many biological applications, especially for packed cells in three-dimensional (3D) microscope images that can fully display cellular morphology. Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional (2D) instance segmentation. However, current methods cannot achieve high segmentation accuracy for irregular cells in 3D images. In this study, we introduce a universal, morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice (C1M2), which can segment cells from a wide range of image types and does not require nucleus images. C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells. Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.

An impedance based microfluidic sensor for evaluation of individual red blood cell solute permeability

—In this paper, we investigate a microfluidic based sensing device for cell membrane permeability measurements in real time with applications in rapid assessment of red blood cell (RBC) quality at the individual cell level. The microfluidic chip was designed with unique abilities to line up the RBCs in the centerline of the microchannel using positive dielectrophoresis (p-DEP) forces, rapid mixing of RBCs with various media (e.g. containing permeating or nonpermeating solutes) injected from different inlets to achieve high mixing efficiency. The chip detects the impedance values of the RBCs within 0.19 s from the start of mixing with other media, at ten electrodes along the length of the channel and enables time series measurements of volume change of individual cell caused by cell osmosis in anisosmotic fluids over a 0.8 s postmixing timespan. This technique enables estimating water permeability of individual cell accurately. Here we first present confirmation of a linear voltage-diameter relationship in polystyrene bead standards. Next, we show that under equilibrium conditions, the voltage-volume relationship in rat red blood cells (RBCs) is linear, corresponding to previously published Boyle van ‘t Hoff plots. Using rat cells as a model for human, we present the first measurement of water permeability in individual red blood cells and confirm that these data align with previously published population level values for human RBC. Finally, we present preliminary evidence for possible application of our device to identify individual RBCs infected with Plasmodium falciparum malaria parasites. Future developments using this device will address the use of whole blood with non-homogenous cell populations, a task currently performed by clinical Coulter counters.

Individual Cell-Level Temperature Monitoring of a Lithium-Ion Battery Pack.

The work described herein details the deployment of an optical fibre strand with five fibre Bragg grating (FBG) sensors for individual cell-level temperature monitoring of a three-cell lithium-ion battery pack. A polymer guide tube with 3D printed plinths is employed, resulting in high precision temperature readings with an average error of 0.97 °C, 1.33 °C, and 1.27 °C for FBG sensors on each battery cell, surpassing traditional thermocouple and platinum resistance sensors in some circumstances. The temperature response of FBGs positioned between battery cells demonstrates that, in addition to sensing temperature at the cell level, temperature data can be effectively acquired between cells, suggesting that FBGs may be used to monitor the heat radiated from individual cells in a battery pack.