4, LOSCHMIDT LABORATORIES 1 Microfluidics - „Lab on a Chip BÍ7430 Molecular Biotechnology Outline □ introduction to microfIuidics □ physics of micro-scale □ lab on a chip applications ■ life and medical science ■ protein and metabolic engineering □ design and fabrication □ sensing and detection 2/35 Lab on a Chip Concept pre-treatment incubation analysis preparation 4 3/35 Microfluidics □ „ behavior, control and manipulation of fluids geometrically constrained to a small dimensions" dimensions (l'-100' jim) volumes (nl_, pL, fl_) unrivalled precision of control (ultra)high analytical throughput reduced sample and power consumption facile process integration and automation Nature 507, 181 (2014) 4/35 Revolution in Electronics 5/35 Revolution in Science? Year Nature Biotechnol. 21, 1179 (2003) 6/35 Concepts in microfluidics □ continuous-flow microfluidics manipulation of continuous liquid flow through m i c r o-f a b r i c a t e d channels □ droplet-based microfluidics manipulating discrete volumes of fluids in immiscible phases □ digital microfluidics droplets manipulated on a substrate using e I e et r o - w ett i n g < oooooooo< 7/35 Novel Physics of Micro-Scale □ viscosity, surface tension and capillary forces dominate Nature Biotechnol. 20, 826 (2002) Appl. Phys. Lett. 83, 4664 (2003) PNAS 99, 16531 (2002) 8/35 Lab on a Chip applications □ analytics and chemistry □ PCR and sequencing □ point of care diagnostics □ pharmacology □ clinical studies □ single cell biology □ p rote i n science 9/35 Polymerase chain reaction □ classical PCR ■ slow h e a t i n g/co o I i n g cycles ■ PCR tubes (strips), 96-well MTP ■ volume 50 to 500 ju L mum Digital polymerase chain reaction □ digital PCR 1 nanoliter droplets 20 000 droplets per run 11/35 Next-generation sequencing □ p a ra 11 e I i za t i o n of single molecule py ros e q u e n c i n g □ 454 Pyrosequencing (Roche) water in oil droplets 1 picoliter (10~12 liters) 1 mil. reads/run Frederick Sanger Nobel Prize in 1 980 12/35 Revolution in Science? □ 2003: 13 years, 3 billion USD □ 2018: days, < 1,000 USD 13/35 Organ(oid)s on chip □ 3D chips mimicking human's physiological responses (e.g., pathological, pharmacokinetic, toxicologica I) □ realistic in vitro model closer to in vivo cell environment (e.g., mechanical strain, patterning, fluid shear stresses) □ replacing expensive and controversial animal testing flat surface micropillar Nature 471, 661-665 (2011) Biophysical Journal 94(5) 1854-1866 Science 364, 960-965 (2019) 14/35 16/35 Protein Engineering RATIONAL DESIGN 1. Computer aided design 2. Site-directed mutagenesis Individual mutated gene 3. Transformation 4. Protein expression 5. Protein purification 6. not applied DIRECTED EVOLUTION 1. not applied IMPROVED ENZYME 2. Random mutagenesis KJ Library of mutated genes (>10,000 clones ) 3. Transformation 4. Protein expression 5. not applied 6. Screening and selection - stability selectivity affinity activity Constructed mutant enzyme 7. Biochemical testing Selected mutant enzymes 18/35 CO Cti O I s 5» e n < d h CO > i 91 I g n < JO a -■: i Še g < "0 0 s x 5>! M f :;■ 1 pi co '■: o 'k is o ID "1 «: 70 O co :■: o 3 'I ■-9 13 CO 2 E 50 "j "1 pi Z r CD Z z < M t4 h > CD CD CD > CD O o o K • ; x ň > 50 t- CD > cd m x 1-3 a ►i s ffj "• Q n 0 pi CO 1 w 0 M > :•: 1 ■ i-3 o pi > x ó M CD I u o N to t—" Ln 0 Ol 0 IX 1 •1 ''i 1 ň 1 t-i 51 ::■ 50 > CD M a U •-3 0 Z t- Z CO :;• 50 < r •-: n a 0 n pi XI CD c ►< < CD 0 < > > t- z Í' •-: t« ■ ' •-: -.: O < > Z D ■ ■ c • - ■v n M >4 pj -: 1 M 2 50 pi < O CJ a a "0 a r- ?! •-: 0 >-* 0 t- rn » y- ÍJ ,.: > pj i' < ft H H K Hj 0 to pj x ' j m :>: x < < > O O 3 ►«1 pi n * > K > > '■: 50 K "1 U 5» >-< >t O pi r 3 a z > H :: H > K •0 g > J "J i ■ > s S» > > x 0 Si ►< D 'J-. •»1 •o x * ň h < n 0 O •< > pi "o Tl < CI H m 1 CO s :•: < □ H > Z > * O n ■■■■ > < n W i M x n > CO 0 a > "0 < CD s n > 0 E Z 55 5^ pj u w 0 ! 1 "0 1 " 1 1 00 1 U) rv. tg 0 Ul 0 01 0 0 0 0 0 0 0 Number of sequences (x106) o cn No. of annotated proteins (x106) Unexplored protein diversity Q CD 3 O 3 o a 0) 0) O" 03 tfi CD (/) (UItra)High-throughput screening Robotic |j,FI iridic Reaction volume 100 nl_ 5 pL Reactions / day 50 000 1 .108 Total time 5 years 3 days Total volume 5 000 L 150 mL No. of plates / devices 250 000 2.0 No. of tips 28 000 000 10 10° 101 102 103 104 105 Fluorescence Intensity Lab Chip 2009, 9: 1850 Anal. Chem. 2014, 86: 2526 20/35 Enzyme specificity profiling Anal. Chem. 2019, 91: 10008-10015 21/35 8888888 Steady-state kinetics 8 Conventional (iFluidic Reaction volume (mL) 2 0.00010 Total enzyme (mg) 1 0.01 Throughput per hour 5 10 000 2.9 - a) 2.4 - o c d) S 1.9 - 0) i_ o E 1.4 -0.9 - 50 100 150 200 Time (s) Small 2015, 11: 4009 n o 3 n fD 01 if o 3 2.1 - 8 18 -c o 0) 1.5 - 0 1 1.2 - 0.9 - —i— 50 100 150 Time (s) 200 ^Fluidic Conventional 1 2 3 4 5 Substrate (mM) 22/35 Mechanism of enzyme catalysis 1 2 4»« >1 LI L2 1— c E + S Z± ESEI ► EP E + P Stopped-flow IxFluidic Dead time 0.3 ms 0.7 ms Reaction volume 100 nL 10 pL Temp, equilibration 10 min 50 ms Signal integration 0.5 ms no limit K CMOS controller «nrjera TL LP 23/35 24/35 3419503^6579455945 dc-rcp _ ^c3t,TCP,(K)-Dcr x CnhaA x ctcp ^at,TCP,(5)-ncP x CphaA x Ctcp d* (CTCP + KmJCp) (CTCP + ^m/TCp) dC(jj)-DCF _ kcat,TCP,(ft)-DCP X cDhaA X CTCP ^cat,(fl)-DCP X cHheC X C(R)-DCP c't CTCP + ^m,TCP c(ä)-DCP + ^m,(R)-DCP dC(,;)_DĽP _ &cat,tcp,(.S)-dcp X cDliaA X CTCF _ ^cat,M-DĽP X cHIieĽ X CM-dcp dt ctcp + Kmj£p c(j)-dcp + íŕm>(s)-dcp d^ECH _ ^catOO-DCP x cHhei: X c(í?)-dcp ^ ^cat,(S)-DCP x cHheC X C(S)-DCP ^cat.ECH X cEchA X CECH dt C((j)_dcp + ^m,(B)-DCP C(S)-DCP + ^m,(S)-DCP CgCH + ^m.ECH dcCpD ^ /icst.ECH X cEchA X cTiCH ^cat.CPD X cHheC X CCPD dt CECH + /rmECH ccpd + ^m,CPD díGDL ^cat.CPD x CHheC x CCPD ^cat.GDL x cEctiA x cGm. dt cCPD + /fm,CPD cGDL + /fm,GD1 x(l+^ + ^) dCcLY ^cat,t;DL X cEchA X C[;DL dt CcDľ. + ^ra.GDl. x (1 + ^Y + ^CP) 25/35 Metabolie engineering CI CIXCI H L D H H D Cl Cl i OH Cl EH H H D EH OH Cl i OH HO OH HOj^OH A grob acte r i um dt c(í)-DCP + ^m,(S)-DCP d^ECH _ ^cat^íO-DCP X cHhet: X C(Í?)-DĽP ^ ^cat,(S)-DCP X cHheC X cCf)-DCP ^cat.ECH X cEchA X CECH dt C((j)_DCP + ^m,(B)-DCP C(S)-DCP + ^m,(S)-DCP CECH + ^ím.ECH dCcPD 'ícat.ECH X cEcľlA X CECH 'fcaCCPD * cHheC X CCPD dt CECH + /ŕmECH dcGDL ^cat.CPD x cHheC x CCPD CCPD + ^m,CPD ^cat.GDL x cEctiA x cGm. d L CCPD + VCPD cGDL +■ /ŕm,GDL X (l + ^ + Íra) def- cat,UDL X cEchA A Ľ[;DL X Cr: dt Ccm. + ^m.GDl. X (1 + j£Y + J^CP) 1.5 dcTCP ^cat,TCP,(fi)-Dcr x cnhiiA X CTCp ^cat,TCP,(j)-ncP x cd1iíia x cTCP dt (CTCP + ^ra.TCp) (cTCP + KmjCP) 0.5 dC(/i)-DCP kcat,TCP,(«)-DCP X cDhaA X CTCp kcat.W)-DCP y cIlheC X C(R}-DCP d L CTCP + -Km.TCP C(J!)-DCP +~ m,(R)-DCP ^cat,TCP,(S)-DCP x cDhaA X cTCP kcat,(S)-DĽP x cHlieC x C(S)-DCP 0 Conversion: 56.83%. ratio: 0.90 : 0.07 : 0.03 100 150 tíme [min] Es ch e r ich i a EchA HheC OD* ChemBioChem 15: 1891 (2014) ACS Synth. Biol. 3: 172 (2014) 26/35 Metabolic engineering HLD „ HHD CI CI CIXCI^CIX0H CI EH HHD EH OH CI I OH HO OH HOj^OH HLD □ 1 nl_ droplet volume □ 10 000 assays/hour ft Artificial Intelligence 0,08 0.07 0.06 0.05 0.04 0.03 36 38 40 42 44 46 Temperature (°C) DhaA/mg 27/35 Design and fabrication □ soft lithography originates from semiconductor industry 28/35 Design and fabrication □ direct fabrication methods 3D PRINTING LASER CUTTING CNC ji-MILLING 29/35 Design and fabrication □ materials inert and transparent P D M S - p o I y (d i m et h y I siloxane) PMMA - poly(methyl m e t h a c r y I a t e) fused silica, quartz and glass □ surface modification plasma treatment silanization sol-gel coating V - .Si. O' -in Quartz 200 nm 156.4° 118.2" 98.2* 85.6" 67.8' 37 2° 12.0° <10° I_c 10|mb 10 pun 30/35 Sensing and detection .m POMS ". parabolic micromirror 80nm gold nanospheres 62.Sum core optical fiber □ processing of small reagent volumes □ analytical timescale and performance □ on chip detection fluorescence ( L S M , FCS, FLIM) UV/VIS absorbance IR spectroscopy Raman scattering (c h e m o/e I e ct r o) luminescence thermal conductivity Rl variation □ off chip detection GC, HPLC, MS N M R, X-ray 31/35 Commercial Solutions Nature Meth. 10, 1003 (2013) Nature 499, 505 (2013) 32/35 Cone usions □ reduced sample/reagent/power consumption □ superior performance and novel physics □ applications in life and medical sciences □ in-house as well as commercial technologies microfluidics revolutionize science 33/35 34/35 Reading □ Mazurenko, S., 2020: Machine Learning in Enzyme Engineering. ACS Catalysis, 10, 1210-1223 □ 3. DATABASES RELEVANT TO ENZYME ENGINEERING 3.3. Emerging Methods for High- Throughput Data Collection (page 1213 - 1216) 'Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic ■International Centre for Clinical Research, St. Ann's Hospital, 602 00 Brno, Czech Republic Machine Learning in Enzyme Engineering Stanislav Mazurenko,*'^ Zbyněk Prokop,' and Jiň Damborsky1'" S Cite This: ACS Catol. 2020, 10, 1210-1223 35/35