31:T886, embed({ "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "width": Math.min(width, 700), "autosize": "fit", "height": Math.min(width*.8, 425), "description": "PC-3 Response to Various Doses of Rotenone", "data": { values: data }, "transform": [ {"calculate": "datum.trel / 60", "as": "trel_hrs"}, { "frame": [-15, 15], "window": [ { "field": "ocr", "op": "mean", "as": "ocr_mean" } ], "groupby": ["dilution"] }, { "frame": [-15, 15], "window": [ { "field": "ocr", "op": "stderr", "as": "ocr_err" } ], "groupby": ["dilution"] } ], "encoding": { "x": {"field": "trel_hrs", "type": "quantitative", "scale" : {"domain": [0, tmax_hrs]}, "axis": {"title": "Time After Dose (hrs)"} } }, "layer": [ { "mark": {"type": "errorband", "extent": "ci", clip: true}, "encoding": { "y": { "field": "ocr", "type": "quantitative", "axis": {"title": "OCR (fmols/mm²/s)"} }, "color": {"field": "dilution", "type":"ordinal", "scale": scale, legend:"bottom"}, } }, { "mark": {"type": "line", clip: true}, "encoding": { "y": { "aggregate":"mean", "field": "ocr_mean", "type": "quantitative", "axis": {"title": "OCR (fmols/mm²/s)"} }, "color": {"field": "dilution", "type":"ordinal", "scale": scale, legend:"bottom"}, } }, /* { "mark": "rule", "encoding": { "x": { "value": 20}, "size": {"value": 5}, "color":{"value": "#ff9900"}, "opacity": {"value":0.2} }, // "stroke": null, // "fill": {"value": "#ff9900"}, // "fillOpacity": {"value":"0.5"} } */ //{ // "mark": {type: "line"} // "y": {"aggregate": "mean","field": "ocr", "type": "quantitative"}, // "color": {"field": "dilution", "type":"ordinal", "scale": scale} //} ] })32:T731,function doseResponsePlot(sample, desc) { return embed({ "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "width": width, "autosize": "fit", "height": Math.min(width, 400), "description": desc, "data": { values: sample }, //"transform": [{"filter": "datum.symbol==='GOOG'"}], "encoding": { "x": {"field": "concentration", "type": "ordinal", "scale" : {"type": "pow", "exponent": 0.3}, "axis": {"title": "Concentration (nM)"} }, }, "layer": [ { "mark": {"type": "errorbar", "extent": "ci", size:120, rule: {size: 40, opacity:0.2}, ticks: {height:20, size: 40, opacity:1}}, //"mark": {"type": "boxplot", "zindex":1, opacity:0.7, size: 40, extent:1}, "encoding": { "y": { "field": "ocr", "type": "quantitative", "axis": {"title": "OCR (fmols/mm²/s)"}, // "scale": {"domain": [0, 100]} }, //"size": {"value":20}, "color": {"field": "dilution", "type":"ordinal", "scale": scale, legend:"bottom"}, } }, //{ // "mark": {"type": "point"}, // "encoding": { // "y": { // "field": "ocr", // "type": "quantitative", // "axis": {"title": "OCR (fmols/mm²/s)"} // }, // "size": {"value":8}, //"color": {"field": "dilution", "type":"ordinal", "scale": scale, legend:"bottom"}, //} //}, { "mark": {type: "point", shape: "diamond", size: 200, color: "black"}, "encoding": { "y": {"aggregate": "mean","field": "ocr", "type": "quantitative"} } }, { "mark": {type: "line", opacity: 0.4, zindex: -200}, "encoding": { "y": {"aggregate": "mean","field": "ocr", "type": "quantitative"} } } ] }) }33:T812,function doseResponsePlotAt(timeHrs) { let sample = getSampleAt(timeHrs) let title = "Dose Response at " + timeHrs + " hours" let barWidth = Math.min(width/20, 25) let plotWidth = Math.min(width, 650) return embed({ "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "width": plotWidth, "autosize": "fit", "height": Math.min(plotWidth, 400), "description": title, "title": title, "data": { values: sample }, //"transform": [{"filter": "datum.symbol==='GOOG'"}], "encoding": { "x": {"field": "concentration", "type": "ordinal", "scale" : {"type": "pow", "exponent": 0.3}, "axis": {"title": "Concentration (nM)"} }, }, "layer": [ { "mark": {"type": "errorbar", "extent": "ci", size:120, rule: {size: barWidth, opacity:0.2}, ticks: {height:20, size: barWidth, opacity:1}}, //"mark": {"type": "boxplot", "zindex":1, opacity:0.7, size: 40, extent:1}, "encoding": { "y": { "field": "ocr", "type": "quantitative", "axis": {"title": "OCR (fmols/mm²/s)", "tickMinStep": 10}, // "scale": {"domain": [0, 100]} }, //"size": {"value":20}, "color": {"field": "dilution", "type":"ordinal", "scale": scale, legend:"bottom"}, } }, //{ // "mark": {"type": "point"}, // "encoding": { // "y": { // "field": "ocr", // "type": "quantitative", // "axis": {"title": "OCR (fmols/mm²/s)"} // }, // "size": {"value":8}, //"color": {"field": "dilution", "type":"ordinal", "scale": scale, legend:"bottom"}, //} //}, { "mark": {type: "point", shape: "diamond", size: 200, color: "black"}, "encoding": { "y": {"aggregate": "mean","field": "ocr", "type": "quantitative"} } }, { "mark": {type: "line", opacity: 0.4, zindex: -200}, "encoding": { "y": {"aggregate": "mean","field": "ocr", "type": "quantitative"} } } ] }) } 34:[{"id":"47e106d75584f84bd2a2bff6deab566f7033ac6829032d09318b62b383f25576ed17c066183270b95adebe81961c28b2d09fee99cdec0e458242c0daa99a8a01","href":"https://static.observableusercontent.com/files/47e106d75584f84bd2a2bff6deab566f7033ac6829032d09318b62b383f25576ed17c066183270b95adebe81961c28b2d09fee99cdec0e458242c0daa99a8a01","name":"device_w600px.png","type":"image/png","lastModified":1580404717681.24,"size":194444},{"id":"b4c0ded548dafb83e042857d9ca3f208b7b7552608fadda83cdfa9d46b12b77a249aa3870ddfb43dd43f5b2e2f8d635ea65225615a939cb7ebc8e11848fa7f84","href":"https://static.observableusercontent.com/files/b4c0ded548dafb83e042857d9ca3f208b7b7552608fadda83cdfa9d46b12b77a249aa3870ddfb43dd43f5b2e2f8d635ea65225615a939cb7ebc8e11848fa7f84","name":"rotenone@1.json","type":"application/json","lastModified":1580338717966.535,"size":1459414}] 2f:{"title":"Rotenone Dose-Response with Continuous OCR Measurements","cells":[{"id":0,"value":"md`# Rotenone Dose-Response with Continuous OCR Measurements`","pinned":false,"hidden":false,"mode":"ojs"},{"id":370,"value":"md`\n${await FileAttachment(\"device_w600px.png\").image()} \n\nThis notebook describes the results of a dose-response experiment - a common experiment performed in cell biology research to determine the toxicity of a candidate drug treatment or a compound with industrial applications. \n\nThe desired results of these experiments are one or more dose-response curves. These curves relate an applied treatment amount to a cellular response as measured by some particular assay.\n\nThe experiment data in this notebook was recorded using [Lucid Scientific's](https://lucidsci.com) RESIPHER system which continuously measures cellular oxygen consumption rates (OCR) in standard multiwell plates.\n\n \n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2202,"value":"md`\n\nSince the RESIPHER's OCR measurements correspond to an [existing non-continuous endpoint assay](https://www.abcam.com/extracellular-oxygen-consumption-assay-ab197243.html), we can directly compare it's measurements to those produced using this functional assay.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2219,"value":"md`## Experiment`","pinned":false,"hidden":false,"mode":"ojs"},{"id":731,"value":"md`For this experiment, we selected [Rotenone](https://pubchem.ncbi.nlm.nih.gov/compound/Rotenone) as our treatment compound and [PC-3](https://www.atcc.org/Products/All/CRL-1435.aspx) as our cell type. Rotenone is a pesticide with a [known pathway of toxicity](https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/rotenone), and has even been linked to Parkinson's disease. And since Rotenone disrupts mitochondrial function and since cellular respiration is an indicator of mitochondrial health, OCR should be a good measure of the cellular response.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2460,"value":"md`### Treatments`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2463,"value":"md`For this experiment, we treated the cells with various concentrations of rotenone and a control treatment of DMSO. The highest rotenone concentration was 2 uM. This concentration was then diluted down by factors of 3 until reaching our lowest concentration of ~8 nM.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2497,"value":"html`\n\n \n ${dilution_values.map(d => {\n let concentration = 0\n let treatment = 'Rotenone'\n let dilution = d.text\n let DIL_HIGHEST = 2000\n if (d.text != 'DMSO') {\n treatment = 'Rotenone'\n dilution = d.text\n concentration = Math.round(DIL_HIGHEST / parseInt(d.text))\n } else {\n treatment = 'DMSO'\n dilution = 'N/A'\n }\n return html`\n \n \n \n \n \n `\n })}\n

Treatment	Dilution	Concentration (nM)
${treatment}	${dilution}	${concentration}

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":371,"value":"md`### Traditional Procedure\n\nAn outline of the traditional procedure for performing this dose-response is:\n\n1. Seed cells\n2. Culture 24 hours\n3. Apply doses of Rotenone at various concentrations/dilutions (2 uM diluted down by factors of 3 and DMSO as control)\n4. Perform the [OCR assay](https://www.abcam.com/Extracellular-Oxygen-Consumption-Assay-ab197243.html) after some period of time (say 24-48 hours)\n\n### New Procedure\n\nWith our RESIPHER continuously measuring OCR throughout the experiment, though, step 4 of the traditional procedure is quite different. We don't have to choose an arbitrary timepoint to perform an invasive, destructive assay so we can let our experiment run as long or short as we'd like.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1467,"value":"md`## Results`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2085,"value":"md`For this experiment, we let the RESIPHER continuously measure OCR for 48 hours after applying the Rotenone treatments. Let's suppose for a moment that we had performed our traditional assay using reagents and a plate reader. We would've had to choose an arbitrary timepoint to assay and effectively end our experiment. Let's say we chose 24 hours after dosing.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":448,"value":"doseResponsePlotAt(24)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2249,"value":"md `The dose-response curve after 24 hours shows OCRs at dose levels of 8 and 25 nM in-line with our DMSO control while higher concentrations show a marked decrease. This dose response curve does not indicate a rotenone toxicity at the lower concentrations.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":661,"value":"md`Let's suppose instead of 24 hours we had performed our assay after 2 days.` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":659,"value":"doseResponsePlotAt(48)","pinned":false,"hidden":false,"mode":"ojs"},{"id":669,"value":"md`The dose-response curve looks similar after 2 days as it did when we assayed after just 1 day. Noting the absolute values, there's even a greater separation between the low and high concentrations. Between these two curves, it would appear that Rotenone does not affect oxygen consumption pathways at concentrations around 25 nM and below.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":695,"value":"md`## What we missed out on with the traditional assay`","pinned":false,"hidden":false,"mode":"ojs"},{"id":699,"value":"md`Unfortunately, those two curves don't give a complete picture of the dose response. Here's a timelapse of the OCR after dosage sampled continuously using the RESIPHER:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":504,"value":"viewof sampleTime = Scrubber(sampleTimes, {autoplay: false, loop:false, delay:150})","pinned":false,"hidden":false,"mode":"ojs"},{"id":489,"value":"doseResponsePlotAt(sampleTime)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1792,"value":"md`If we look at the dose response continually throughout the experiment, the respiration rate is certainly affected by the rotenone even at the low concentrations of 8 and 25 nM. The cells do recover somewhat, but there is some *dynamic* response we missed with our traditional endpoint assay. This dynamic is visually apparent when we view the data as a time series.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":201,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2176,"value":"md`Clearly there is something going on during the first day after dosing that warrants further investigation...`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2450,"value":"md`______`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2452,"value":"md`## APPENDIX`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1162,"value":"md`### Analysis Code`","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"data = FileAttachment('rotenone@1.json').json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1237,"value":"SAMPLE_INT_MINS = 1","pinned":false,"hidden":false,"mode":"ojs"},{"id":435,"value":"function chooseSampleAt(t_hrs_after_dose) {\n return data.filter((d) => d.trel == t_hrs_after_dose*60)\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":441,"value":"sample = chooseSampleAt(24)","pinned":false,"hidden":false,"mode":"ojs"},{"id":443,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1671,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":278,"value":" dilution_values = [{color: \"#ff7575\", text: \"1\"},\n {color: \"#ed4fac\", text: \"3\"},\n {color: \"#bf49e0\", text: \"9\"},\n {color: \"#8665fc\", text: \"27\"},\n {color: \"#5899f7\", text: \"81\"},\n {color: \"#47d1d1\", text: \"243\"},\n {color: \"#000000\", text: \"DMSO\"}]\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":280,"value":"scale = {\n return { \n \"domain\": dilution_values.map((v) => v.text),\n \"range\": dilution_values.map((v) => v.color) \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2476,"value":"treatment_dataset = dilution_values.map((d) => {\n let concentration = 0\n let treatment = 'Rotenone'\n let dilution = d.text\n let DIL_HIGHEST = 2000\n if (d.text != 'DMSO') {\n treatment = 'Rotenone'\n dilution = d.text\n concentration = Math.round(DIL_HIGHEST / parseInt(d.text))\n } else {\n treatment = 'DMSO'\n dilution = 'N/A'\n }\n return {'Treatment': treatment, 'Dilution': dilution, 'Concentration (nM)': concentration}\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":491,"value":"sampleTimes = Array.from({length: 49}, (_, i) => i)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1676,"value":"sampleSetsByHour = sampleTimes.map(t_hr => data.filter((d) => d.trel == t_hr*60))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1683,"value":"function getSampleAt(t_hrs) {\n return sampleSetsByHour[t_hrs]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"tzero = moment(\"2019-12-05 12:00\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1226,"value":"tmin_hrs = 0","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"tmax_hrs = 48","pinned":false,"hidden":false,"mode":"ojs"},{"id":1871,"value":"toDollar = function(cost, pts) {\n return '$ ' + new Number(cost).toFixed(pts) \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1349,"value":"md`### Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"moment = require('moment')","pinned":true,"hidden":false,"mode":"ojs"},{"id":180,"value":"embed_DEP = {\n const embed = await require('vega-embed@4'),\n loader = await require('vega-loader-arrow@0.0.7');\n embed.vega.formats('arrow', loader); // register arrow as a supported format\n return embed;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1641,"value":"embed = require(\"vega-embed@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"import {printTable} from \"@lucid/data-utilities\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":177,"value":"import {vl} from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":336,"value":"//import {slider, menu, checkbox} from '@jheer/dom-utilities'","pinned":true,"hidden":false,"mode":"ojs"},{"id":579,"value":"import {slider} from \"@jgrunik/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":488,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":2472,"value":"import {table} from \"@tmcw/tables@513\"\n\n","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}