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PURPOSE To investigate the safety and efficacy of cultured human corneal endothelial cell (hCEC)-injection therapy with mature differentiated (mature) cell subpopulations (SPs) for corneal endothelial failure (CEF). DESIGN Comparative, interventional case series. METHODS This study involved 18 eyes with CEF that underwent cultured hCEC-injection therapy, categorized into two groups: 1) 11 eyes administered a relatively lower proportion (0.1 to 76.3%) of mature cell SPs [Group 1 (Gr1)], and 2) 7 eyes administered a relatively higher proportion (>90%) of mature cell SPs [Group 2 (Gr2)]. From 1-week to 3-years postoperative, corneal endothelial cell (CEC) density (CECD), central corneal thickness (CCT), and best-corrected visual acuity (BCVA) were recorded, and the CEC parameter's 'spring constant' was calculated. The proportion of mature SPs was evaluated by fluorescence-activated cell sorting analysis based on cell-surface markers. RESULTS At 3-years postoperative, corneal restoration with improved BCVA was attained in 10 of the 11 Gr1 eyes and all Gr2 eyes, the median CECD in Gr2 (3,083 cells/mm2; range, 2,182-4,417 cells/mm2) was higher than that in Gr1 (1,349 cells/mm2; range, 746-2,104 cells/mm2) (P < 0.001), and the spring constant verified the superiority of the mature cultured hCECs. From 24-weeks through 3-years postoperative, the median percentage of CECD decrease was 3.2% in Gr2 and 23.6% in Gr1 (P < 0.005). CCT recovery was prompt and constant in Gr2, while diverse in Gr1. No adverse events were observed. CONCLUSION Our findings showed that mature cell SPs for hCEC-injection therapy provide rapid recovery of CCT, better CECD, and low CECD attrition over 3 years post surgery.

The extraordinary advances in clinical hematology, biology, and oncology in the last decades would not have been possible without discovering how to identify and count the cells circulating in the blood. For centuries, scientists have used slides, counting chambers (hemocytometers), and diluting and staining solutions for this task. Then, automated hemocytometry began. This science, now linked to the daily routine of laboratory hematology, has completed an overwhelming path over a few decades. Our laboratories today operate with versatile multiparameter systems, ranging from complex single-channel instruments to bulky continuous flow machines. In terms of clinical information obtained from a simple routine blood test, the full exploitation of their potential depends on the operators' imagination and courage. A comprehensive review of the scientific publications that have accompanied the development of hemocytometry from the 1950s to today would require entire volumes. More than seven hundred contributions that authors worldwide have published in Clinical and Laboratory Haematology until 2007 and then the International Journal of Laboratory Hematology are summarized. Such journals have represented and hopefully will continue to represent the privileged place of welcome for future scientific research in hemocytometry. Improved technologies, attention to quality, new reagents and electronics, information technology, and scientist talent ensure a more profound and deeper knowledge of cell properties: current laboratory devices measure and count even minor immature or pathological cell subpopulations. Full-field hemocytometry includes the analysis of nonhematic fluids, digital adds to the microscope, and the development of effective point-of-care devices.

We provide a short review of existing models with multiple taxis performed by (at least) one species and consider a new mathematical model for tumor invasion featuring two mutually exclusive cell phenotypes (migrating and proliferating). The migrating cells perform nonlinear diffusion and two types of taxis in response to non-diffusing cues: away from proliferating cells and up the gradient of surrounding tissue. Transitions between the two cell subpopulations are influenced by subcellular (receptor binding) dynamics, thus conferring the setting a multiscale character. We prove global existence of weak solutions to a simplified model version and perform numerical simulations for the full setting under several phenotype switching and motility scenarios. We also compare (via simulations) this model with the corresponding haptotaxis-chemotaxis one featuring indirect chemorepellent production and provide a discussion about possible model extensions and mathematical challenges., Comment: 43 pages, 15 figures

A method has been developed for assessing the proportion of lysed MDA-MB-231 cells cultured in the multi-organ human-on-a-chip model based on the determination of the hsa-miR-222-3p and hsa-miR-99b-5p microRNA levels in the culture medium. The threshold levels of microRNA expression were calculated, which made it possible to estimate the proportion of lysed cells with an accuracy of 25%. microRNA, breast cancer, cell model This work was supported by the Ministry of Education and Science of the Russian Federation (grant RFMEFI61618X0092).

Dermal fibroblasts are a dynamic and diverse population of cells whose functions in skin in many respects remain unknown. Normal adult human skin contains at least three distinct subpopulations of fibroblasts, which occupy unique niches in the dermis. Fibroblasts from each of these niches exhibit distinctive differences when cultured separately. Specific differences in fibroblast histophysiology are evident in papillary dermal fibroblasts, which reside in the superficial dermis, and reticular fibroblasts, which reside in the deep dermis. Both of these subpopulations of fibroblasts differ from the fibroblasts that are associated with hair follicles. Fibroblasts engage in fibroblast-epidermal interactions during hair development and in interfollicular regions of skin. They also play an important role in cutaneous structural transformations., Дермальные фибробласты – динамическая и разнообразная популяция клеток, функции которых в коже остаются неизвестными во многих отношениях. Нормальная зрелая кожа человека содержит, по крайней мере, три различные субпопуляции фибробластов, занимающих характерные локусы в дерме. Фибробласты каждого из этих локусов демонстрируют характерные отличия при раздельном культивировании. В папиллярных дермальных фибробластах, расположенных в поверхностной дерме, и в ретикулярных фибробластах, содержащихся в глубокой дерме, выявляются специфические гистофизиологическое различия. Обе эти субпопуляции фибробластов отличаются от фибробластов, ассоциированных с волосяными фолликулами. Фибробласты принимают участие в фибробласт-эпидермальных взаимодействиях при развити волос и в интерфолликулярных участках кожи. Они также играют важную роль в структурных перестройках кожи., Дермальні фібробласти – динамічна і різноманітна популяція клітин, функції яких у шкірі залишаються невідомими в багатьох відношеннях. Нормальна зріла шкіра людини містить, принаймні, три різні субпопуляції фібробластів, що займають характерні локуси в дермі. Фібробласти кожного з цих локусів демонструють характерні відмінності при відокремленому культивуванні. У папілярних дермальних фібробластах, що розташовані в поверхневій дермі, і в ретикулярних фібробластах, що містяться в глибокій дермі, виявляються специфічні гістофізіологічні відмінності. Обидві ці субпопуляції фібробластів відрізняються від фібробластів, що асоційовані з волосяними фолікулами. Фібробласти беруть участь у фібробласт-епідермальних взаємодіях під час розвитку волосся й в інтерфолікулярних ділянках шкіри. Вони також відіграють важливу роль у структурних перебудовах шкіри.

This paper reviews the use of dielectrophoresis for high-fidelity separations and characterizations of subpopulations to highlight the recent advances in the electrokinetic field as well as provide insight into its progress toward commercialization. The role of cell subpopulations in heterogeneous clinical samples has been studied to deduce their role in disease progression and therapy resistance for instances such as cancer, tissue regeneration, and bacterial infection. Dielectrophoresis (DEP), a label-free electrokinetic technique, has been used to characterize and separate target subpopulations from mixed samples to determine disease severity, cell stemness, and drug efficacy. Despite its high sensitivity to characterize similar or related cells based on their differing bioelectric signatures, DEP has been slowly adopted both commercially and clinically. This review addresses the use of dielectrophoresis for the identification of target cell subtypes in stem cells, cancer cells, blood cells, and bacterial cells dependent on cell state and therapy exposure and addresses commercialization efforts in light of its sensitivity and future perspectives of the technology, both commercially and academically.

Synovial biopsy techniques have developed and widely expanded over the past few years, in particular due to the development of ultrasound-guided procedures. This article reviews the different techniques, clinical applications, and the latest advances in translational research as well as current challenges and perspectives. The first part focuses on different techniques available for biopsy, along with their feasibility, success rate, tolerance, and training requirements. In the second part, clinical applications are described. Data on diagnostic performances are reported, especially regarding septic arthritis. Translational research applications are described and explained in the final part, from the early histological studies and the first description of pathotype to more recent technologies involving -omics. Latest developments involving single-cell RNA sequence analysis have allowed the discovery of new cell subpopulations with remarkable roles in RA pathophysiology. These studies pave the ground for the discovery of new therapeutic targets and the implementation of personalized therapy in RA. Key Point•This review provides an overview of synovial biopsy techinques and applications especially in clinical and translational research.

The recent wide-spread adoption of single-cell profiling technologies has revealed that individual cancers are not homogenous collections of deregulated cells, but instead comprise multiple genetically and phenotypically distinct cell subpopulations that exhibit a wide range of responses to extracellular signals and therapeutic insult. Such observations point to the urgent need to understand cancer as a complex, adaptive system. Cancer systems biology studies seek to develop the experimental and theoretical methods required to understand how biological components work together to determine how cancer cells function. Ultimately, such approaches will lead to improvements in how cancer is managed and treated. In this review, we discuss recent advances in cancer systems biology approaches to quantify, model, and elucidate mechanisms of heterogeneity.

A detector of cell subpopulations developed at the Institute of Analytical Instrumentation of the Russian Academy of Sciences was used for immunophenotyping lymphocytes from healthy donors and standardized blood samples with normal and low-level cell populations. It has been shown that it is possible (provided that the modern criteria for the accuracy of the analysis are fulfilled) to determine the relative content of the main populations of lymphocytes, including in standard samples with reduced content of T helper cells that mimic the blood of patients with HIV infection.

Single-cell RNA-seq is being increasingly applied in complex study designs, commonly spanning multiple individuals, conditions, or tissues. Analysis of such heterogeneous collections requires a way of identifying recurrent cell subpopulations. We developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

Simple Summary Different types of cells with tumor-initiating cell (TIC) activity contribute to colorectal cancer (CRC) progression and resistance to anti-cancer treatment. In this study, we aimed to understand whether different cell types exist within a patient-derived tumor culture, distinguishable by different patterns of their gene expression. By mRNA sequencing of patient-derived CRC cultures at the single-cell level, we defined expression programs that closely resemble differentiated cell populations of the normal intestine. Here, cell type-associated subpopulations showed differences in functional properties such as cell growth and energy metabolism. Subsequent functional analyses in vitro and in vivo demonstrated that metabolic states are linked to TIC activity in primary CRC cultures. We also show that TIC activity is dependent on oxidative phosphorylation, which may therefore represent a target for novel therapies. Abstract Intra-tumor heterogeneity of tumor-initiating cell (TIC) activity drives colorectal cancer (CRC) progression and therapy resistance. Here, we used single-cell RNA-sequencing of patient-derived CRC models to decipher distinct cell subpopulations based on their transcriptional profiles. Cell type-specific expression modules of stem-like, transit amplifying-like, and differentiated CRC cells resemble differentiation states of normal intestinal epithelial cells. Strikingly, identified subpopulations differ in proliferative activity and metabolic state. In summary, we here show at single-cell resolution that transcriptional heterogeneity identifies functional states during TIC differentiation. Furthermore, identified expression signatures are linked to patient prognosis. Targeting transcriptional states associated to cancer cell differentiation might unravel novel vulnerabilities in human CRC.

Tumour spheroids are widely used to pre-clinically assess anti-cancer treatments. They are an excellent compromise between the lack of microenvironment encountered in adherent cell culture conditions and the great complexity of in vivo animal models. Spheroids recapitulate intra-tumour microenvironment-driven heterogeneity, a pivotal aspect for therapy outcome that is, however, often overlooked. Likely due to their ease, most assays measure overall spheroid size and/or cell death as a readout. However, as different tumour cell subpopulations may show a different biology and therapy response, it is paramount to obtain information from these distinct regions within the spheroid. We describe here a methodology to quantitatively and spatially assess fluorescence-based microscopy spheroid images by semi-automated software-based analysis. This provides a fast assay that accounts for spatial biological differences that are driven by the tumour microenvironment. We outline the methodology using detection of hypoxia, cell death and PBMC infiltration as examples, and we propose this procedure as an exploratory approach to assist therapy response prediction for personalised medicine.

Machine learning and single-cell data inform personalized drug combinations that selectively co-inhibit cancer cell populations., The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.

International audience; Bioimpedance measurements using microfluidic systems play an increasingly important role in cell analysis. In this paper, a microfluidic device mimicking the vasculature, in particular blood microcapillaries, and equipped with electrodes for bioimpedance sensing is described. Red blood cells, from patients suffering from sickle cell disease, were analyzed and discriminated in this device according to the physical parameters. The statistical position of different subpopulations on the Gaussian distribution of the whole sickle cell population was analyzed. A discrimination between sickle cell subpopulations, related to different density states, was achieved.

Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.

Multiple infection (when a single cell can become super-infected with multiple copies of virus) can effect evolutionary processes in HIV infection, such as the generation and spread of different mutations. Synaptic transmission (where multiple copies of virus can be transferred during a single cell-to-cell interaction) leads to an increase in infection multiplicity. Here, we analyze the effect of multiple infection on the evolution of an HIV infection using a hybrid stochastic-deterministic algorithm. This algorithm allows us to stochastically simulate a large number of cells and large number of cell subpopulations in a computationally efficient way. Specifically, we classify each cell subpopulation as "large" or "small" based on some size threshold (for which we provide an analytical lower bound), and then simulate the small populations stochastically, while simultaneously using the deterministic equations for the large populations. We first demonstrate to what extent deterministic models are incomplete in predicting mutant dynamics, and then use the hybrid method to study aspects of mutant evolution. Focusing on the mutant populations at the time of peak infection, we find that overall, multiple infection promotes the existence of mutant strains of virus, both in terms of faster generation and overall number. It also increases the variance in mutant numbers and results in a larger probability to observe rare strains, such as triple mutants. In the context of multiple infection, synaptic transmission is superior at generating mutants, as it increases the number of transcription events that occur. This effect is observed for neutral and advantageous mutants, even in the presence of interference. For disadvantageous mutants, purely synaptic or purely free virus transmission pathways result in an increased number of mutants, but in the presence of complementation, purely synaptic transmission maximizes mutant production and spread.

CD4+ T lymphocytes consist of naïve, antigen-specific memory, and memory-phenotype (MP) cell compartments at homeostasis. We recently showed that MP cells exert innate-like effector function during host defense, but whether MP CD4+ T cells are functionally heterogeneous and, if so, what signals specify the differentiation of MP cell subpopulations under homeostatic conditions is still unclear. Here we characterize MP lymphocytes as consisting of T-bethigh, T-betlow, and T-bet− subsets, with innate, Th1-like effector activity exclusively associated with T-bethigh cells. We further show that the latter population depends on IL-12 produced by CD8α+ type 1 dendritic cells (DC1) for its differentiation. Finally, our data demonstrate that this tonic IL-12 production requires TLR-MyD88 signaling independent of foreign agonists, and is further enhanced by CD40-CD40L interactions between DC1 and CD4+ T lymphocytes. We propose that optimal differentiation of T-bethigh MP lymphocytes at homeostasis is driven by self-recognition signals at both the DC and Tcell levels.

The aim of the present study was to evaluate histological and stereological changes, as well as the variations in the number and size of cells from diverse cell subpopulations in testes of newly hatched chicks treated with follicle stimulating hormone (FSH) and luteinizing hormone (LH) during embryonic development. Stereological results indicated that in FSH-treated chicks total volume of the tubular compartment constitutes most of the testis. In contrast, the total volume of interstitial tissue constitutes most of the testis of LH-treated chicks. Results indicate that the number of germ and Sertoli cells increases as a result of FSH and LH treatment, but in FSH-treated testis, Sertoli cells were the most numerous cell type in seminiferous tubules; whereas germ cells were the most numerous cell type in testis of LH-treated chicks. Results also indicate there was a larger total volume of Leydig cells in the testes of FSH- and LH-treated chicks. The larger volume of Leydig cells in FSH-treated chicks is due to a larger cellular volume of these cells, and not due to the number, which remains constant. In contrast, in testes of LH-treated chicks, there is a larger number and volume of Leydig cells. These results indicate the testes of chick embryos respond to FSH and LH treatment, with there being modifications in the seminiferous tubules and interstitial tissue, but these changes differ markedly, indicating that FSH and LH have differential effects on chick testes.

Recent research on cancer metabolism has revealed that individual tumors have highly heterogeneous metabolic profiles that contribute to the connective metabolic networks within the tumor and its environment. Indeed, tumor-associated cells types, including tumor cells, cancer-associated fibroblasts (CAFs) and immune cells, reprogram their metabolism in many different ways due to diverse genetic backgrounds and complex environmental stimuli. This intratumoral metabolic heterogeneity and the derived metabolic interactions play an instrumental role in cancer progression. Understanding how this heterogeneity occurs may provide promising therapeutic strategies. Here, we review the diverse metabolic profiles of several important cell subpopulations in tumors and their impact on tumor progression and discuss the consequent metabolic interactions as well as the related therapeutic concerns.

Single cell technologies provide an unprecedented opportunity to explore the heterogeneity in a biological process at the level of single cells. One major challenge in analyzing single cell data is to identify cell subpopulations, stable cell states, and cells in transition between states. To elucidate the transition mechanisms in cell fate dynamics, it is highly desirable to quantitatively characterize cellular states and intermediate states. Here, we present scRCMF, an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent substructures from a gene-cell matrix. We incorporate a random coefficient matrix-based regularization into the standard nonnegative matrix decomposition model to improve the reliability and stability of estimating latent substructures. To quantify the transition capability of each cell, we propose two new measures: single-cell transition entropy (scEntropy) and transition probability (scTP). When applied to two simulated and three published scRNA-seq datasets, scRCMF not only successfully captures multiple subpopulations and transition processes in large-scale data, but also identifies transition states and some known marker genes associated with cell state transitions and subpopulations. Furthermore, the quantity scEntropy is found to be significantly higher for transition cells than other cellular states during the global differentiation, and the scTP predicts the “fate decisions” of transition cells within the transition. The present study provides new insights into transition events during differentiation and development.

Although cancerous tumours usually originate from a single cell, they normally evolve into a remarkably heterogeneous agglomeration of cells. Heterogeneity is a pervasive and almost universal feature of tumours, but its origin and consequences remain poorly understood. Tumour heterogeneity has been usually associated with poor prognosis, but a better understanding of it may lead to more personalized diagnosis and therapy. Here, we study tumour heterogeneity developing a computational model in which different cell subpopulations compete for space. The model suggests that aggressive tumour subpopulations may become even more aggressive when they grow with a non-aggressive subpopulation. The model also provides a mechanistic explanation of how heterogeneity drives growth. In particular, we observed that even a mild heterogeneity in the proliferation rates of different cell subpopulations leads to a much faster overall tumour growth when compared to a homogeneous tumour. The proposed model may be a starting point to study tumour heterogeneity computationally and to suggest new hypotheses to be tested experimentally. This article is part of the theme issue ‘Patterns in soft and biological matters’.

The advent of single-cell RNA sequencing (scRNA-seq) techniques opens up new opportunities for studying the cell-specific changes in the transcriptomic data. An important research problem related with scRNA-seq data analysis is to identify cell subpopulations with distinct functions. However, the expression profiles of individual cells are usually measured over tens of thousands of genes, and it remains a difficult problem to effectively cluster the cells based on the high-dimensional profiles. An additional challenge of performing the analysis is that, the scRNA-seq data are often noisy and sometimes extremely sparse due to technical limitations and sampling deficiencies. In this paper, we propose a biclustering-based framework called DivBiclust that effectively identifies the cell subpopulations based on the high-dimensional noisy scRNA-seq data. Compared with nine state-of-the-art methods, DivBiclust excels in identifying cell subpopulations with high accuracy as evidenced by our experiments on ten real scRNA-seq datasets with different size and diverse dropout rates. The supplemental materials of DivBiclust, including the source codes, data, and a supplementary document, are available at https://www.github.com/Qiong-Fang/DivBiclust.

Microfluidics has proven to be a powerful tool for probing biology at the single-cell level. However, it is only in the past 5 years that single-cell microfluidics has been used in the field of virology. An array of strategies based on microwells, microvalves, and droplets is now available for tracking viral infection dynamics, identifying cell subpopulations with particular phenotypes, as well as high-throughput screening. The insights into the virus–host interactions gained at the single-cell level are unprecedented and usually inaccessible by population-based experiments. Therefore, single-cell microfluidics, which opens new avenues for mechanism elucidation and development of antiviral therapeutics, would be a valuable tool for the study of viral pathogenesis.

The intratumor heterogeneity represents one of the most difficult challenges for the development of effective therapies to treat pediatric glioblastoma (pGBM) and diffuse intrinsic pontine glioma (DIPG). These brain tumors are composed of heterogeneous cell subpopulations that coexist and cooperate to build a functional network responsible for their aggressive phenotype. Understanding the cellular and molecular mechanisms sustaining such network will be crucial for the identification of new therapeutic strategies. To study more in-depth these mechanisms, we sought to apply the Multifluorescent Marking Technology. We generated multifluorescent pGBM and DIPG bulk cell lines randomly expressing six different fluorescent proteins and from which we derived stable optical barcoded single cell-derived clones. In this study, we focused on the application of the Multifluorescent Marking Technology in 2D and 3D in vitro/ex vivo culture systems. We discuss how we integrated different multimodal fluorescence analysis platforms, identifying their strengths and limitations, to establish the tools that will enable further studies on the intratumor heterogeneity and interclonal interactions in pGBM and DIPG.

Rheumatoid arthritis (RA) risk has a large genetic component (similar to 60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.

Bacterial persistence is a phenomenon in which a subpopulation of cells survives antibiotic treatment. The occurrence of bacterial persisters is associated with recurrence of chronic infections. In this study, we aimed to isolate, characterize persister subpopulation in Proteus mirabilis. Persister cells isolation was done by the dose-dependent killing of ciprofloxacin. Their characterization was achieved by determining their growth rates. Our results revealed that 1.3% of persister cells could be recovered from the Proteus mirabilis test isolate. Upon resuscitation, these cell subpopulations exhibited slow growth rate than wild-type cells. As a common phenomenon demonstrated among microbial pathogens, Proteus mirabilis persisters could be isolated with ciprofloxacin. The slow growth rate is one of its characters recorded in the study for persister cells of such bacterial species.