Ticks carry several human pathogenic microbes including Borreliae and Flavivirus causing tick-born encephalitis. Ticks can also carry DNA of Chlamydia-like organisms (CLOs). The purpose of this study was to investigate the occurrence of CLOs in ticks and skin biopsies taken from individuals with suspected tick bite. DNA from CLOs was detected by pan-Chlamydiales-PCR in 40% of adult ticks from southwestern Finland. The estimated minimal infection rate for nymphs and larvae (studied in pools) was 6% and 2%, respectively. For the first time, we show CLO DNA also in human skin as 68% of all skin biopsies studied contained CLO DNA as determined through pan-Chlamydiales-PCR. Sequence analyses based on the 16S rRNA gene fragment indicated that the sequences detected in ticks were heterogeneous, representing various CLO families; whereas the majority of the sequences from human skin remained “unclassified Chlamydiales” and might represent a new family-level lineage. CLO sequences detected in four skin biopsies were most closely related to “uncultured Chlamydial bacterium clones from Ixodes ricinus ticks” and two of them were very similar to CLO sequences from Finnish ticks. These results suggest that CLO DNA is present in human skin; ticks carry CLOs and could potentially transmit CLOs to humans.

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high‐throughput single‐cell fluorescent imaging. However, fluorescent labeling is costly and time‐consuming. We present a computational method called DeepIFC based on the Inception U‐Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single‐cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

The authors wish to make the following modification to this paper [...]