flow cytometric data acquisition of microbial aggregates

Imaging Flow Cytometry를 사용한 미생물 autoaggregation의 정량 분석

Bacterial autoaggregation is considered a primary step in biofilm formation. In this process (sometimes also called autoagglutination or flocculation), bacteria of the same type form multicellular clumps that eventually settle at the bottom of culture tubes or attach to their target tissue or surface1.
Autoaggregation is a widely observed phenomenon and has been shown so far in Gram-negative pathogens such as the opportunistic pathogen Acinetobacter baumannii2, the dental pathogen Aggregobacter actinomycetemcomitans3, and the emerging pathogen Burkholderia pseudomallei4. Autoaggregation has also been described in several probiotic Gram-positive strains5,6,7,8. In Lactobacillus (L.) acidophilus, autoaggregation was partially mediated by S-layer proteins and correlated with an adhesion to xylan7. Similarly, we found a correlation between glucose-dependent autoaggregation and induction of the adhesive properties (as judged by adhesion to mucin) of the probiotic species Lacticaseibacillus rhamnosus GG, Lacticaseibacillus casei, L. acidophilus, Lacticaseibacillus paracasei, and Lactiplantibacillus plantarum5. The increased autoaggregation most likely reflected changes in the expression of cellular adhesins in response to glucose and its catabolites9. While the molecular mechanisms of autoaggregation remain to be determined, it has been shown that this process alters the phenotype of the aggregated bacteria and grants them enhanced tolerance to environmental stressors1, as well as increased sensitivity to quorum sensing molecules10.
Several approaches have been used to measure autoaggregation; one experimental approach is to let cultures stand statically in narrow culture tubes for a given time. Control cultures remain turbid, whereas autoaggregation cultures will settle at the bottom of the tube. A more quantitative approach measures autoaggregation by sedimentation or settling assay11.
Flow cytometry has also been increasingly employed in recent years to investigate bacterial autoaggregation. This method is appropriate for analyzing particles between approximately 0.5 and 1000 &#181;m in size. The single bacterium or formed aggregates are suspended in fluid, fed into a stream, and can be detected one by one11. Recording forward scattered light allows measuring the relative size of the cell or aggregate. It is relatively fast and straightforward but cannot detect several parameters, such as aggregate size or the average number of cells in aggregates. Therefore, this approach can be complemented microscopically, allowing more parameters to be checked12. However, traditional microscopy is time-consuming and thereby limits the number of tested samples and the statistical power of the analysis. In general, imaging flow cytometry provides several features compared to traditional flow cytometry, such as simultaneous analysis of cell morphology and phenotype, conducting image-based analysis, detecting rare events, and validating flow cytometry data13. These advantages enhance the capabilities of flow cytometry and facilitate a more detailed examination of cell populations.
This study provides a valuable protocol for imaging flow cytometry to monitor autoaggregation in lactic acid bacteria (LAB). These Gram-positive rods are facultative anaerobes and belong to the LAB group. These efficient glucose fermenters generate lactic acid as their main end-product of carbohydrate metabolism14. These bacteria are beneficial core members of the microbiome and are naturally found in the gastrointestinal tract (GIT) of humans and animals, as well as in the urogenital tract of females15. Therefore, the exact characterization of their autoaggregation properties is of high biotechnological and clinical interest.
Our previous findings indicated that the basal level of autoaggregation differs between different probiotic strains. This heterogeneity is affected by the different carbohydrates used as a carbon source5. To overcome this fundamental property of probiotic bacteria, the effects of carbohydrates from the diet were monitored on autoaggregation on the single-cell level using IFC. This IFC-based approach combines the power and speed of traditional flow cytometers with the microscope&#39;s resolution. Therefore, it allows high-rate complex morphometric measurements in a phenotypically defined way16,17. This approach can be extended to other probiotic and pathogenic bacteria, combined with fluorescent reporters to monitor gene expression, and fluorescently labeled strains to monitor the presence and abundance of specific bacterial species in heterogeneous aggregates.

저자 스포트라이트: Advancing Research in Microbial Autoaggregation Using Imaging Flow Cytometry (이미징 유세포분석을 사용한 미생물 자가응집 연구의 발전)

미생물 자가응집(microbial autoaggregation)을 연구하기 위한 이미징 유세포분석(Imaging flow cytometry)

This study utilized Lactobacillus species as a model to demonstrate the effectiveness of imaging flow cytometry analysis in studying microbial autoaggregation. The focus is on analyzing the response of autoaggregation of Lactobacillus species to simple carbohydrates from the diet. The most used methods for measuring autoaggregation are spectral photometry, which measures microbial suspensions, turbidity, or optical density, and traditional microscopy techniques, such as bright field, phase contrast, and fluorescent microscopy.When talking about hight-throughput imaging, one major experimental challenge is single event segmentation. In this protocol, it is a single aggregate. Imaging flow cytometry addresses this challenge elegantly and give researchers a powerful tool for studying autoaggregation.

미생물 응집체의 유세포 분석 데이터 수집

유세포 분석을 시작하려면 먼저 암시야 측정을 위해 785나노미터 레이저를 5밀리와트의 전력으로 설정합니다. 개구수가 0.9인 60X 렌즈를 사용하십시오. 그런 다음 고감도 설정을 선택하고 속도를 낮게 설정합니다.캘리브레이션 비드 안정성을 보장하려면 유체 상자에서 Play 버튼을 누르고 선명하고 선명하게 보여야 하는 비드 이미지를 검사합니다. 새 산점도(New Scatterplot)를 선택하여 작업 공간 상자에 새 산점도를 생성합니다. 그런 다음 Brightfield 채널에서 SSC 채널 강도에 해당하는 영역을 선택하고 샘플 내에서 동시에 실행되는 보정 비드를 제외합니다.그런 다음 Load 버튼을 누르고 Lactobacilli 샘플이 들어있는 튜브를 홀더에 넣습니다. Acquisition Settings(수집 설정) 상자에 샘플 이름을 입력하고 데이터 저장 위치를 지정합니다. 일반적으로 10, 000에서 20, 000 이벤트 범위 내에서 수집할 이벤트 수를 설정합니다.이제 Acquisition 상자에 있는 Record 버튼을 클릭하여 Acquisition 프로세스를 시작합니다. 프로세스는 지정된 이벤트 볼륨에 도달한 후 중단됩니다. Return 버튼을 눌러 튜브를 언로드하고 팝업 창의 지시에 따라 홀더에서 제거합니다.모든 샘플에 대해 이 과정을 반복한 후 데이터 수집 균일성을 유지하기 위해 템플릿을 저장합니다. 데이터 분석의 경우 원시 파일을 분석 소프트웨어에 로드하려면 파일을 클릭하고 RIF 파일을 엽니다. 열린 창에서 Use Acquisition Analysis를 선택한 다음 OK를 클릭합니다. 다음으로, 그래디언트 평균 제곱근의 히스토그램을 만들려면 새 히스토그램 버튼을 클릭하고 분석할 수집의 모집단을 선택합니다.X Axis feature(X 축 기능)에서 Brightfield channel(명시야 채널)의 Gradient RMS를 선택합니다. 다음으로, 분석 영역에서 Create Line Region 버튼을 클릭하여 초점이 맞지 않는 이벤트를 제외하는 게이트를 배치합니다. 분석 영역에서 New Scatterplot 버튼을 클릭하여 면적 대 종횡비의 산점도를 생성합니다.새 산점도 창에서 집중된 모집단을 선택합니다. 그런 다음 X축 기능에 대한 명시야 채널 영역을 선택합니다. Y-축 피처의 종횡비를 선택합니다.집계 이벤트 모집단의 area 값을 기반으로 Create Rectangle 버튼을 사용하여 산점도에 게이트를 그립니다. 마찬가지로, singles 및 small aggregates 모집단에 대한 또 다른 게이트를 그립니다. 파일 탭을 클릭하고 템플릿 ast로 저장을 선택하여 데이터 분석을 템플릿으로 저장한 다음 동일한 템플릿 아래의 다음 샘플 파일을 열어 데이터 분석 파일을 만듭니다.집계 크기 분포를 측정하려면 먼저 집계 이벤트 모집단의 영역에 대한 히스토그램을 그립니다. 파일을 클릭하고 Save As Template(템플릿으로 저장) 옵션을 선택하여 데이터 분석을 저장합니다. 저장한 후 데이터 분석 파일에 대한 동일한 템플릿 아래의 다음 샘플 파일을 엽니다.LGG와 L.paracasei에서 단일세포, 작은 응집체, 큰 응집체 및 사슬이 관찰되었으나 사슬과 큰 응집체를 구별할 수 없었다. 발효성 또는 비발효성 설탕에 대한 반응에서 다양한 LAB 균주의 응집 특성에서 상당한 변화가 관찰되었습니다.

flow-cytometric-data-acquisition-of-microbial-aggregates

Research

JoVE Journal

Biology

Flow Cytometric Data Acquisition of Microbial Aggregates

Imaging Flow Cytometry to Study Microbial Autoaggregation

Beneficial and probiotic bacteria play essential roles in their hosts, providing various health benefits, including immunity to infectious diseases. The Lactobacillaceae family consists of Gram-positive bacteria with confirmed probiotic properties. This study utilizes Lactobacillaceae species as a model to demonstrate the effectiveness of single-cell high throughput analysis in studying cellular aggregation. The focus is on analyzing the response of these beneficial species to simple carbohydrates from the diet.
The study showcases how Imaging Flow Cytometry (IFC) can overcome the fundamental differences in the assembly of probiotic bacteria in the presence and absence of carbohydrates. IFC combines the power and speed of conventional flow cytometry with the spatial resolution of microscopy, enabling high-rate complex morphometric measurements in a phenotypically defined manner across a library of beneficial bacterial strains and conditions. This protocol provides insights into the autoaggregation of Lactobacillaceae species and sheds light on their response to dietary carbohydrates, contributing to understanding the mechanisms behind the beneficial effects of these probiotic bacteria.

This protocol describes a quantitative approach to measure microbial autoaggregation using imaging flow cytometry.

Beneficial and probiotic bacteria play essential roles in their hosts, providing various health benefits, including immunity to infectious diseases. The ...

연구

생물학

Lactobacillaceae Sample Preparation for Flow Cytometric Analysis

Start by inoculating five milliliters of lactobacilli MRS broth with a single colony of the lactobacilli strain, incubated at 37 degrees Celsius under static conditions overnight. Dilute the cell culture in a one-to-100 ratio in three milliliters of 50%supplemented tryptic soy broth, vortex the culture, and incubate it. Then invert the tube to mix the cells in the medium before transferring 200 to 300 microliters of the sample into a 1.5 milliliter micro centrifuge tube.

락토바실러스과(Lactobacillaceae) 유세포 분석을 위한 시료 전처리

To begin the flow cytometric analysis, first set the 785-nanometer laser to a power of five milliwatts for dark field measurements. Use a 60X lens with a numerical aperture of 0.9. Then select the high-sensitivity setting and set the speed to low.To ensure calibration bead stability, press the Play button in the fluidics box and examine the bead images, which should appear sharp and clear. Generate a new scatterplot in the workspace box by selecting New Scatterplot. Then select the area in the Brightfield channel that corresponds to the SSC channel intensity and exclude the calibration beads that are running concurrently within the sample.Next, press the Load button and position a tube containing the Lactobacilli sample in the holder. In the Acquisition Settings box, enter the sample name and specify the location for data storage. Set the number of events to be collected, typically within the range of 10, 000 to 20, 000 events.Now, begin the acquisition process by clicking the Record button located in the Acquisition box. The process will halt after reaching the specified volume of events. Press the Return button to unload the tube and remove it from the holder as prompted in the popup window.After repeating the process for all samples, save the template to maintain data acquisition uniformity. For data analysis, to load the raw file into the analysis software, click on File and open the rif file. In the opened window, select Use Acquisition Analysis and then click OK.Next, to create a histogram of the gradient root mean square, click the New Histogram button and choose the population from the acquisition to analyze.Under the X Axis feature, select Gradient RMS of the Brightfield channel. Next, place a gate to exclude non-focused events by clicking the Create Line Region button in the analysis area. Create a scatterplot of the area versus the aspect ratio by clicking the New Scatterplot button in the analysis area.Select the focused population in the New Scatterplot window. Then select the area of the Brightfield channel for the X-Axis feature. Choose the aspect ratio for the Y-Axis feature.Draw a gate on the scatterplot using the Create Rectangle button based on the area value for the aggregate events population. Similarly, draw another gate for the singles and small aggregates population. Save your data analysis as a template by clicking the File tab, choosing Save As Template ast, and then open the next sample file under the same template to create the data analysis file.To measure the aggregate size distribution, start by plotting a histogram of the area of the aggregation events population. Save the data analysis by clicking on the file and selecting the Save As Template option. After saving, open the next sample file under the same template for the data analysis file.Single cells, small aggregates, large aggregates, and chains were observed in LGG and L.paracasei but it was not possible to distinguish between chains and larger aggregates. Significant variations in the aggregation characteristics of different LAB strains were observed in response to fermentable or non-fermentable sugars.