High-throughput single-cell sequencing of activated sludge microbiome Article Swipe
Yulin Zhang
,
Bingjie Xue
,
Yanping Mao
,
Xi Chen
,
Weifu Yan
,
Yan-Ren Wang
,
Yulin Wang
,
Lei Liu
,
Jiale Yu
,
Xiaojin Zhang
,
Chao Shan
,
Edward Topp
,
Wenshan Zheng
,
Tong Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ese.2024.100493
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ese.2024.100493
Wastewater treatment plants (WWTPs) represent one of biotechnology's largest and most critical applications, playing a pivotal role in environmental protection and public health. In WWTPs, activated sludge (AS) plays a major role in removing contaminants and pathogens from wastewater. While metagenomics has advanced our understanding of microbial communities, it still faces challenges in revealing the genomic heterogeneity of cells, uncovering the microbial dark matter, and establishing precise links between genetic elements and their host cells as a bulk method. These issues could be largely resolved by single-cell sequencing, which can offer unprecedented resolution to show the unique genetic information. Here we show the high-throughput single-cell sequencing to the AS microbiome. The single-amplified genomes (SAGs) of 15,110 individual cells were clustered into 2,454 SAG bins. We find that 27.5% of the genomes in the AS microbial community represent potential novel species, highlighting the presence of microbial dark matter. Furthermore, we identified 1,137 antibiotic resistance genes (ARGs), 10,450 plasmid fragments, and 1,343 phage contigs, with shared plasmid and phage groups broadly distributed among hosts, indicating a high frequency of horizontal gene transfer (HGT) within the AS microbiome. Complementary analysis using 1,529 metagenome-assembled genomes from the AS samples allowed for the taxonomic classification of 98 SAG bins, which were previously unclassified. Our study establishes the feasibility of single-cell sequencing in characterizing the AS microbiome, providing novel insights into its ecological dynamics, and deepening our understanding of HGT processes, particularly those involving ARGs. Additionally, this valuable tool could monitor the distribution, spread, and pathogenic hosts of ARGs both within AS environments and between AS and other environments, which will ultimately contribute to developing a health risk evaluation system for diverse environments within a One Health framework.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ese.2024.100493
- OA Status
- gold
- Cited By
- 6
- References
- 99
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402531612Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ese.2024.100493Digital Object Identifier
- Title
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High-throughput single-cell sequencing of activated sludge microbiomeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-13Full publication date if available
- Authors
-
Yulin Zhang, Bingjie Xue, Yanping Mao, Xi Chen, Weifu Yan, Yan-Ren Wang, Yulin Wang, Lei Liu, Jiale Yu, Xiaojin Zhang, Chao Shan, Edward Topp, Wenshan Zheng, Tong ZhangList of authors in order
- Landing page
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https://doi.org/10.1016/j.ese.2024.100493Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.ese.2024.100493Direct OA link when available
- Concepts
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Activated sludge, Throughput, Microbiome, Computational biology, DNA sequencing, Biology, Computer science, Environmental science, Genetics, Gene, Sewage treatment, Telecommunications, Environmental engineering, WirelessTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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99Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.largely | 83 |
| abstract_inverted_index.largest | 8 |
| abstract_inverted_index.matter, | 63 |
| abstract_inverted_index.matter. | 146 |
| abstract_inverted_index.method. | 78 |
| abstract_inverted_index.monitor | 244 |
| abstract_inverted_index.pivotal | 15 |
| abstract_inverted_index.plasmid | 156, 164 |
| abstract_inverted_index.playing | 13 |
| abstract_inverted_index.precise | 66 |
| abstract_inverted_index.samples | 194 |
| abstract_inverted_index.spread, | 247 |
| abstract_inverted_index.advanced | 42 |
| abstract_inverted_index.analysis | 186 |
| abstract_inverted_index.contigs, | 161 |
| abstract_inverted_index.critical | 11 |
| abstract_inverted_index.elements | 70 |
| abstract_inverted_index.insights | 223 |
| abstract_inverted_index.presence | 142 |
| abstract_inverted_index.removing | 33 |
| abstract_inverted_index.resolved | 84 |
| abstract_inverted_index.species, | 139 |
| abstract_inverted_index.transfer | 179 |
| abstract_inverted_index.valuable | 241 |
| abstract_inverted_index.activated | 25 |
| abstract_inverted_index.clustered | 119 |
| abstract_inverted_index.community | 135 |
| abstract_inverted_index.deepening | 229 |
| abstract_inverted_index.dynamics, | 227 |
| abstract_inverted_index.frequency | 175 |
| abstract_inverted_index.involving | 237 |
| abstract_inverted_index.microbial | 46, 61, 134, 144 |
| abstract_inverted_index.pathogens | 36 |
| abstract_inverted_index.potential | 137 |
| abstract_inverted_index.providing | 221 |
| abstract_inverted_index.represent | 4, 136 |
| abstract_inverted_index.revealing | 53 |
| abstract_inverted_index.taxonomic | 198 |
| abstract_inverted_index.treatment | 1 |
| abstract_inverted_index.Wastewater | 0 |
| abstract_inverted_index.antibiotic | 151 |
| abstract_inverted_index.challenges | 51 |
| abstract_inverted_index.contribute | 266 |
| abstract_inverted_index.developing | 268 |
| abstract_inverted_index.ecological | 226 |
| abstract_inverted_index.evaluation | 272 |
| abstract_inverted_index.fragments, | 157 |
| abstract_inverted_index.framework. | 281 |
| abstract_inverted_index.horizontal | 177 |
| abstract_inverted_index.identified | 149 |
| abstract_inverted_index.indicating | 172 |
| abstract_inverted_index.individual | 116 |
| abstract_inverted_index.pathogenic | 249 |
| abstract_inverted_index.previously | 206 |
| abstract_inverted_index.processes, | 234 |
| abstract_inverted_index.protection | 19 |
| abstract_inverted_index.resistance | 152 |
| abstract_inverted_index.resolution | 92 |
| abstract_inverted_index.sequencing | 105, 215 |
| abstract_inverted_index.ultimately | 265 |
| abstract_inverted_index.uncovering | 59 |
| abstract_inverted_index.distributed | 169 |
| abstract_inverted_index.establishes | 210 |
| abstract_inverted_index.feasibility | 212 |
| abstract_inverted_index.microbiome, | 220 |
| abstract_inverted_index.microbiome. | 109, 184 |
| abstract_inverted_index.sequencing, | 87 |
| abstract_inverted_index.single-cell | 86, 104, 214 |
| abstract_inverted_index.wastewater. | 38 |
| abstract_inverted_index.Furthermore, | 147 |
| abstract_inverted_index.communities, | 47 |
| abstract_inverted_index.contaminants | 34 |
| abstract_inverted_index.environments | 256, 276 |
| abstract_inverted_index.establishing | 65 |
| abstract_inverted_index.highlighting | 140 |
| abstract_inverted_index.information. | 98 |
| abstract_inverted_index.metagenomics | 40 |
| abstract_inverted_index.particularly | 235 |
| abstract_inverted_index.Additionally, | 239 |
| abstract_inverted_index.Complementary | 185 |
| abstract_inverted_index.applications, | 12 |
| abstract_inverted_index.distribution, | 246 |
| abstract_inverted_index.environmental | 18 |
| abstract_inverted_index.environments, | 262 |
| abstract_inverted_index.heterogeneity | 56 |
| abstract_inverted_index.unclassified. | 207 |
| abstract_inverted_index.understanding | 44, 231 |
| abstract_inverted_index.unprecedented | 91 |
| abstract_inverted_index.characterizing | 217 |
| abstract_inverted_index.classification | 199 |
| abstract_inverted_index.biotechnology's | 7 |
| abstract_inverted_index.high-throughput | 103 |
| abstract_inverted_index.single-amplified | 111 |
| abstract_inverted_index.metagenome-assembled | 189 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile.value | 0.92696362 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |