Neurotransmitter Detection Using Corona Phase Molecular Recognition on Fluorescent Single-Walled Carbon Nanotube Sensors

Temporal and spatial changes in neurotransmitter concentrations are central to information processing in neural networks. Therefore, biosensors for neurotransmitters are essential tools for neuroscience. In this work, we applied a new technique, corona phase molecular recognition (CoPhMoRe), to identify adsorbed polymer phases on fluorescent single-walled carbon nanotubes (SWCNTs) that allow for the selective detection of specific neurotransmitters, including dopamine. We functionalized and suspended SWCNTs with a library of different polymers (<i>n</i> = 30) containing phospholipids, nucleic acids, and amphiphilic polymers to study how neurotransmitters modulate the resulting band gap, near-infrared (nIR) fluorescence of the SWCNT. We identified several corona phases that enable the selective detection of neurotransmitters. Catecholamines such as dopamine increased the fluorescence of specific single-stranded DNA- and RNA-wrapped SWCNTs by 58–80% upon addition of 100 μM dopamine depending on the SWCNT chirality (<i>n</i>,<i>m</i>). In solution, the limit of detection was 11 nM [<i>K</i>_d = 433 nM for (GT)₁₅ DNA-wrapped SWCNTs]. Mechanistic studies revealed that this turn-on response is due to an increase in fluorescence quantum yield and not covalent modification of the SWCNT or scavenging of reactive oxygen species. When immobilized on a surface, the fluorescence intensity of a single DNA- or RNA-wrapped SWCNT is enhanced by a factor of up to 5.39 ± 1.44, whereby fluorescence signals are reversible. Our findings indicate that certain DNA/RNA coronae act as conformational switches on SWCNTs, which reversibly modulate the SWCNT fluorescence. These findings suggest that our polymer–SWCNT constructs can act as fluorescent neurotransmitter sensors in the tissue-compatible nIR optical window, which may find applications in neuroscience

Neurotransmitter Detection Using Corona Phase Molecular Recognition on Fluorescent Single-Walled Carbon Nanotube Sensors

Temporal and spatial changes in neurotransmitter concentrations are central to information processing in neural networks. Therefore, biosensors for neurotransmitters are essential tools for neuroscience. In this work, we applied a new technique, corona phase molecular recognition (CoPhMoRe), to identify adsorbed polymer phases on fluorescent single-walled carbon nanotubes (SWCNTs) that allow for the selective detection of specific neurotransmitters, including dopamine. We functionalized and suspended SWCNTs with a library of different polymers (<i>n</i> = 30) containing phospholipids, nucleic acids, and amphiphilic polymers to study how neurotransmitters modulate the resulting band gap, near-infrared (nIR) fluorescence of the SWCNT. We identified several corona phases that enable the selective detection of neurotransmitters. Catecholamines such as dopamine increased the fluorescence of specific single-stranded DNA- and RNA-wrapped SWCNTs by 58–80% upon addition of 100 μM dopamine depending on the SWCNT chirality (<i>n</i>,<i>m</i>). In solution, the limit of detection was 11 nM [<i>K</i>_d = 433 nM for (GT)₁₅ DNA-wrapped SWCNTs]. Mechanistic studies revealed that this turn-on response is due to an increase in fluorescence quantum yield and not covalent modification of the SWCNT or scavenging of reactive oxygen species. When immobilized on a surface, the fluorescence intensity of a single DNA- or RNA-wrapped SWCNT is enhanced by a factor of up to 5.39 ± 1.44, whereby fluorescence signals are reversible. Our findings indicate that certain DNA/RNA coronae act as conformational switches on SWCNTs, which reversibly modulate the SWCNT fluorescence. These findings suggest that our polymer–SWCNT constructs can act as fluorescent neurotransmitter sensors in the tissue-compatible nIR optical window, which may find applications in neuroscience

Supplementary datasets manuscript "Accumulation of heme biosynthetic intermediates contributes to the antibacterial action of the metalloid tellurite"

Supplementary dataset 1. Chemical genomics of TeO32-. Positive or negative chemical genetic interaction scores represent increased or decreased TeO32- resistance, respectively. Supplementary dataset 2. Single nucleotide polymorphisms found in evolved strains EM40, EM41, and EM2

Data File S6. Genetic profile similarity-based hierarchy analysis

The first tab (“Gene to hierarchy cluster mapping”) lists the clusters identified at each level of the genetic interaction-based hierarchy and the deletion and TS allele array mutants assigned to each cluster. Examples of clusters described in the main text are highlighted. The subsequent 9 tabs indicate enrichment of clusters resolved at the specified profile similarity range for specific cell compartments (Cyclops_enrich), biological processes (GO BP_enrich), protein complexes (complex_enrich) and KEGG pathways (KEGG_enrich). The final tab in the file indicates the clusters used to map the functional distribution of negative and positive interactions shown in Fig. 5D

Data File S9. High and low interaction degree genes

This file lists the negative and positive interaction degree associated with every nonessential deletion (sn#), essential TS (tsq#), and DAmP (damp#) query mutant strain screened against the DMA (“query degree X DMA” tab) and/or TSA (“query degree X TSA” tab). A subset of strains were found to carry a second, spontaneous suppressor mutation that affected fitness of the query mutant strain. Strains carrying a suppressor mutation mapped through SGA analysis are indicated (“-supp”). Query mutants comprising the 20% highest and lowest degree groups of strains are indicated. Furthermore, a “Co-batch signal” rank is provided for every query (see “Co-batch filtering of query mutant strains”). Low ranks correspond to evidence for lingering batch effects. Another column, “ Gene with correlated GI profiles that are co-annotated with the query gene (%)", provides the percent of correlated gene pairs that are co-annotated to the particular query. A low negative interaction degree (e.g. 20% lowest negative interaction degree) coupled with a low co-batch rank (e.g. < ~0.2) and a low fraction of correlated pairs that share a similar functional annotation with a given query strain (e.g. < ~0.15) may be indicative of a low confidence screen. However, these criteria should be considered as loose indicators and not definitive metrics of screen quality and thus, should not be used as strict filters on the global interaction dataset. Another list (“Queries removed - batch effects” tab) indicates ~300 query strains that exhibited severe systematic batch effects and thus were removed from the indicated data set. Finally, two additional tabs provide the negative and positive interaction degree associated with every nonessential (“nonessential array degree” tab) and essential (“essential array degree” tab) array mutant, respectively

Data File S5. SAFE analysis_Gene cluster identity and functional enrichments

This file lists the results from SAFE analysis of the global genetic profile similarity network (Fig. 1 and Fig. 2). Functional terms enriched within specific network clusters associated with GO biological processes (14) and/or protein complexes (Data File S12). A list of genes comprising each bioprocess-enriched cluster shown on the global similarity network is also provided. Functional terms enriched within specific network clusters associated with cell compartments (17, 119) are all shown on Fig. 2B

Data File S15. Protein complex interaction enrichment and bias

This file indicates fold enrichment and biases in positive vs. negative interaction frequency for protein complexes and is described in detail above (see ìAnalysis of protein complexes exhibiting a positive interaction enrichment biasî). Rows highlighted in yellow indicate protein complexes that show > 1.5X enrichment for positive interactions (ìE_fold_pos) stronger enrichment for positive versus negative interactions when screened against the essential TSA. The file consists of the following columns: (A) Protein complex name (B) Number of complex member-encoding query genes screened against the DMA (ìqueries_vs_DMAî). (C) Number of complex member-encoding query genes screened against the TSA (ìqueries_vs_TSAî). (D) Nonessential-negative GI fold enrichment (ìN_fold_negî): negative interaction fold enrichment for a complex of interest with nonessential genes not in the complex. (E) Essential-negative GI fold enrichment (ìE_fold_negî): negative interaction fold enrichment for a complex of interest with essential genes not in the complex. (F) Nonessential-positive GI fold enrichment (ìN_fold_posî): positive interaction fold enrichment for a complex of interest with nonessential genes not in the complex. (G) Essential-positive GI fold enrichment (ìE_fold_posî): positive interaction fold enrichment for a complex of interest with essential genes not in the complex. Complexes with a positive GI enrichment > 1.5X are highlighted in yellow. These values were used to generate Fig. 8C. (H) Positive GI bias with essential genes (ìposGI_bias_with_Eî): the relative positive:negative enrichment ratio of essential to nonessential genes for the complex of interest (calculated as [D/E]/[F/G]). Complexes with a positive GI enrichment > 1 and a positive GI bias > 1.5 are highlighted in yellow. These values were used to generate Fig. 8D. (I) Positive GI bias with nonessential genes (posGI_bias_with_Nî): the relative positive:negative enrichment ratio of nonessential to essential genes for the complex of interest (calculated as [F/G]/[D/E])

Data File S16. Genetic suppression analysis

This file includes raw data from spot dilution growth assays to identify positive interactions that can be classified as genetic suppression. The suppression score is based on visual assessment of double mutant strain growth relative to a wild type and single mutant control strains. The score reflects strength of suppression with a score of 4 indicative of a strong suppression interaction where double mutant growth exceeded growth of the sickest single mutant and a score of 0 indicates failure to confirm a suppression interaction

Data File S10. Correlation analysis of query strain GI degree

As a complement to analysis of array strains (fig. S11-S12), GI degrees were calculated for query strains by counting negative interactions (tab 1, interactions with DMA strains; tab 2, interactions with TSA strains) and by counting positive interactions (tab 3, interactions with DMA strains; tab 4, interactions with TSA strains). Essential and nonessential queries were analyzed separately and results are labeled by grouped column headers. Wilcoxon rank-sum tests compared the GI degree in paired gene sets defined by absence and presence of each binary feature tested (top table). If the P-value is significant (< 0.05), the “Test result” column describes the degree of the set of genes for which the listed binary feature is true (compared to the set for which the feature is false). Tests were not performed, indicated by “N/A”, if data were present for fewer than 50 strains; strains with missing data were excluded from the tests. Pearson’s correlation (column labeled “r”) was used to measure associations between GI degree and features that are continuous or counts (bottom table). Uncorrected P-values are shown. The features examined in this analysis are described above (see Methods section entitled, “ Genetic interaction degree and frequency analysis”). Given that analysis of different features required using different statistical tests and some features are not expected to be independent of each other, no multiple hypotheses correction procedures were used. We do note that 31 gene features were tested

Data File S8. Mass spectrometric evidence for Ipa1 interactions

This file lists proteins identified with high confidence as specific physical interactors with strains expressing Ipa1-GFP from its endogenous locus or Ipa1-HA from a galactose-inducible plasmid