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Towards Bacteria Counting in DI Water of Several Microliters or Growing Suspension Using Impedance Biochips
We counted bacterial cells of E. coli strain K12 in several-microliter DI water or in several-microliter PBS in the low optical density (OD) range (OD = 0.05–1.08) in contact with the surface of Si-based impedance biochips with ring electrodes by impedance measurements. The multiparameter fit of the impedance data allowed calibration of the impedance data with the concentration cb of the E. coli cells in the range of cb = 0.06 to 1.26 × 109 cells/mL. The results showed that for E. coli in DI water and in PBS, the modelled impedance parameters depend linearly on the concentration of cells in the range of cb = 0.06 to 1.26 × 109 cells/mL, whereas the OD, which was independently measured with a spectrophotometer, was only linearly dependent on the concentration of the E. coli cells in the range of cb = 0.06 to 0.50 × 109 cells/mL.
Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress
Analyzing the cells in various body fluids can greatly deepen the understanding of the mechanisms governing the cellular physiology. Due to the variability of physiological and metabolic states, it is important to be able to perform such studies on individual cells. Therefore, we developed an optofluidic system in which we precisely manipulated and monitored individual cells of Escherichia coli. We tested optical micromanipulation in a microfluidic chamber chip by transferring individual bacteria into the chambers. We then subjected the cells in the chambers to antibiotic cefotaxime and we observed the changes by using time-lapse microscopy. Separately, we used laser tweezers Raman spectroscopy (LTRS) in a different micro-chamber chip to manipulate and analyze individual cefotaxime-treated E. coli cells. Additionally, we performed conventional Raman micro-spectroscopic measurements of E. coli cells in a micro-chamber. We found observable changes in the cellular morphology (cell elongation) and in Raman spectra, which were consistent with other recently published observations. The principal component analysis (PCA) of Raman data distinguished between the cefotaxime treated cells and control. We tested the capabilities of the optofluidic system and found it to be a reliable and versatile solution for this class of microbiological experiments.
Predictive Modeling of Antibiotic Susceptibility in E. Coli Strains Using the U-Net Network and One-Class Classification
The antibiotic resistance of bacterial pathogens has become one of the most serious global health issues due to misusing and overusing of antibiotics. Recently, different technologies were developed to determine bacteria susceptibility towards antibiotics; however, each of these technologies has its advantages and limitations in clinical applications. In this contribution, we aim to assess and automate the detection of bacterial susceptibilities towards three antibiotics; i.e. ciprofloxacin, cefotaxime and piperacillin using a combination of image processing and machine learning algorithms. Therein, microscopic images were collected from different E. coli strains, then the convolutional neural network U-Net was implemented to segment the areas showing bacteria. Subsequently, the encoder part of the trained U-Net was utilized as a feature extractor, and the U-Net bottleneck features were utilized to predict the antibiotic susceptibility of E. coli strains using a one-class support vector machine (OCSVM). This one-class model was always trained on images of untreated controls of each bacterial strain while the image labels of treated bacteria were predicted as control or non-control images. If an image of treated bacteria is predicted as control, we assume that these bacteria resist this antibiotic. In contrast, the sensitive bacteria show different morphology of the control bacteria; therefore, images collected from these treated bacteria are expected to be classified as non-control. Our results showed 83% area under the receiver operating characteristic (ROC) curve when OCSVM models were built using the U-Net bottleneck features of control bacteria images only. Additionally, the mean sensitivities of these one-class models are 91.67% and 86.61% for cefotaxime and piperacillin; respectively. The mean sensitivity for the prediction of ciprofloxacin is only 59.72% as the bacteria morphology was not fully detected by the proposed method.
Raman spectroscopy follows time-dependent changes in T lymphocytes isolated from spleen of endotoxemic mice
T lymphocytes (T cells) are highly specialized members of the adaptive immune system and hold the key to the understanding the hosts’ response toward invading pathogen or pathogen-associated molecular patterns such as LPS. In this study, noninvasive Raman spectroscopy is presented as a label-free method to follow LPS-induced changes in splenic T cells during acute and postacute inflammatory phases (1, 4, 10, and 30 d) with a special focus on CD4+ and CD8+ T cells of endotoxemic C57BL/6 mice. Raman spectral analysis reveals highest chemical differences between CD4+ and CD8+ T cells originating from the control and LPS-treated mice during acute inflammation, and the differences are visible up to 10 d after the LPS insult. In the postacute phase, CD4+ and CD8+ T cells from treated and untreated mice could not be differentiated anymore, suggesting that T cells largely regained their original status. In sum, the biological information obtained from Raman spectra agrees with immunological readouts demonstrating that Raman spectroscopy is a well-suited, label-free method for following splenic T cell activation in systemic inflammation from acute to postacute phases. The method can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult.T lymphocytes (T cells) are highly specialized members of the adaptive immune system and hold the key to the understanding the hosts’ response toward invading pathogen or pathogen-associated molecular patterns such as LPS. In this study, noninvasive Raman spectroscopy is presented as a label-free method to follow LPS-induced changes in splenic T cells during acute and postacute inflammatory phases (1, 4, 10, and 30 d) with a special focus on CD4+ and CD8+ T cells of endotoxemic C57BL/6 mice. Raman spectral analysis reveals highest chemical differences between CD4+ and CD8+ T cells originating from the control and LPS-treated mice during acute inflammation, and the differences are visible up to 10 d after the LPS insult. In the postacute phase, CD4+ and CD8+ T cells from treated and untreated mice could not be differentiated anymore, suggesting that T cells largely regained their original status. In sum, the biological information obtained from Raman spectra agrees with immunological readouts demonstrating that Raman spectroscopy is a well-suited, label-free method for following splenic T cell activation in systemic inflammation from acute to postacute phases. The method can also be applied to directly study tissue sections as is demonstrated for spleen tissue one day after LPS insult.