Data_Sheet_1_Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.pdf

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.</p

Data_Sheet_2_Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.pdf

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.</p

A method for labeling proteins with tags at the native genomic loci in budding yeast

<div><p>Fluorescent proteins and epitope tags are often used as protein fusion tags to study target proteins. One prevailing technique in the budding yeast <i>Saccharomyces cerevisiae</i> is to fuse these tags to a target gene at the precise chromosomal location via homologous recombination. However, several limitations hamper the application of this technique, such as the selectable markers not being reusable, tagging of only the C-terminal being possible, and a “scar” sequence being left in the genome. Here, we describe a strategy to solve these problems by tagging target genes based on a pop-in/pop-out and counter-selection system. Three fluorescent protein tag (mCherry, sfGFP, and mKikGR) and two epitope tag (HA and 3×FLAG) constructs were developed and utilized to tag <i>HHT1</i>, <i>UBC13</i> or <i>RAD5</i> at the chromosomal locus as proof-of-concept.</p></div

Truncation sequences of fluorescent proteins tags flanked by the <i>URA3</i> gene.

<p>Truncation sequences of fluorescent proteins tags flanked by the <i>URA3</i> gene.</p

PCR analysis of tag-<i>HHT1</i> yeast strains and their subcellular localizations.

<p>(A) HHT1-sfGFP strain. The genomic DNA PCR data shows in the left panel. Lane 1: DNA ladder marker. Lane 2: the original strain yRH182. Lane 3 and 4: the pop-in and pop-out strain. (B) The subcellular localizations of HHT1-sfGFP. The yRH182 (upper) is the wild-type yeast strains without modification; the tagged strains <i>HHT1</i>-sfGFP are shown at the bottom. The images were obtained under Plan-Apochromat 63×/1.40 oil (Zeiss 5 Live).</p

Plasmid constructs for protein-tagging.

<p>The original plasmid is pBluescriptII SK, and the maps display the restriction enzyme sites used for construction. (A) The pCUC map contains the 5’ <i>mChe-URA3-</i>3’ <i>erry</i> cassette. (B) The psfGUG map contains the 5’ <i>sfG-URA3-</i>3’ <i>GFP</i> cassette. (C) The pKUK map contains the 5’ <i>mKik-URA3-</i>3’ <i>kGR</i> cassette. For pCUC, psfGUG and pKUK, each pair of sectional tag sequences has a partially duplicated sequence for homologous recombination. (D) The pHUH map contains the 5’<i>HA-URA3-</i>3’<i>HA</i> cassette. (E) The pFUF map contains the 5’<i>3</i>×<i>FLAG-URA3-</i>3’<i>3</i>×<i>FLAG</i> cassette. For these two plasmids, the HA and FLAG tags are too short to be split, so the full-length sequences are used for homologous recombination.</p

The subcellular localizations of mCherry-Ubc13 and Rad5-sfGFP.

<p>HK578-10A (upper A) and HK578-10D (upper B) are the wild-type yeast strains without modification; the tagged strains <i>P</i>_<i>PGK1</i>-Ubc13-mCherry and <i>P</i>_<i>PGK1</i>-Rad5-sfGFP are shown at the bottom of A and bottom of B, respectively. The nucleus was stained by DAPI. The images were obtained under Plan-Apochromat 63×/1.40 oil (Zeiss LSM780).</p

Proteins tagging primers used in this study^ɑ.

<p>Proteins tagging primers used in this study<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176184#t002fn001" target="_blank">^ɑ</a>.</p

Yeast strains used in this study.

<p>Yeast strains used in this study.</p

PCR analysis of tag-<i>UBC13</i>/<i>RAD5</i> yeast strains.

<p>(A) mCherry-Ubc13 strain. The left panel displays the genomic DNA PCR data. Lane 1: DNA ladder marker. Lane 2: the original strain HK578-10A. Lane 3: the pop-in strain CUC-Ubc13. Lane 4: the pop-out strain mCherry-Ubc13. The right panel represents the corresponding genomic organizations at the locus of <i>UBC13</i> in these three different strains. (B) Rad5-sfGFP strain. The original strain is HK578-10D. (C) Rad5-mKikGR strain. The original strain is HK578-10D. Arrows indicate the locations of primers. The detail information of primers could be found in the part of yeast strains check primers in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176184#pone.0176184.s006" target="_blank">S3 Table</a>.</p