High-Content Screening images streaming analysis using the STriGen methodology

High-Con en Sc eening images s eaming analysis using he ST iGen me hodology Lau a Melga -Ga cía Da a Science & Big Da a Lab, Pablo de Ola ide Uni e si y Se ille, Spain [email p o ec ed] Da id Gu ié ez-A ilés Da a Science & Big Da a Lab, Pablo de Ola ide Uni e si y Se ille, Spain [email p o ec ed] C is ina Rubio-Escude o Depa men o Compu e Science, Uni e si y o Se ille Se ille, Spain [email p o ec ed] Alicia T oncoso Da a Science & Big Da a Lab, Pablo de Ola ide Uni e si y Se ille, Spain [email p o ec ed] ABSTRACT One o he echniques ha p o ides sys ema ic insigh s in o biolog- ical p ocesses is High-Con en Sc eening (HCS). I measu es cells pheno ypes simul aneously. When analysing hese images, ea u es like luo escen colou , shape, spa ial dis ibu ion and in e ac ion be ween componen s can be ound. ST iGen, which wo ks in he eal- ime en i onmen , leads o he possibili y o s udying ime e olu ion o hese ea u es in eal- ime. In addi ion, da a s eam- ing algo i hms a e able o p ocess lows o da a in a as way. In his a icle, ST iGen (S eaming T iclus e ing Gene ic) algo i hm is p esen ed and applied o HCS images. Resul s ha e p o ed ha ST iGen inds quali y iclus e s in HCS images, adap s co ec ly h oughou ime and is as e han e-compu ing he iclus e ing algo i hm each ime a new da a s eam image a i es. CCS CONCEPTS •In o ma ion sys ems → Clus e ing ; Da a s eam mining ; • Compu ing me hodologies → Gene ic algo i hms ; • Applied compu ing → Molecula e olu ion; Imaging;␣ KEYWORDS Real- ime, T iclus e ing, Gene ic ope a o s, High-Con en Sc een- ing 1 INTRODUCTION Nowadays, one o he bigges challenges in biology is unde s anding genes and hei biological ci cui s. Due o ha , echniques ha in es iga e comple e cellula p ocesses a e becoming mo e ele an , as High-Con en Sc eening (HCS). HCS combines an au oma ed imaging and analysis o in ac cells exposed o some pe u ba ions (chemical o genomic) ha al e hei pheno ype [11]. HCS is made by many s eps ha can ake oo much ime i hey a e no e ec i ely done. On he o he hand, hese days, s eam com- pu ing ends a e ising, i.e., algo i hms ha eac in he as es way o p o ide in o ma ion om da a in eal ime. Consequen ly, he p ocessing and analysis o HCS images in a s eaming en i onmen could gi e quick in o ma ion om hem. In [ 8 ] he T iGen algo i hm is p esen ed as a T iclus e ing algo- i hm ha disco e s g oups o 3D da ase s h oughou ins ances, a ibu es and ime. In [ 12 ] ST iGen algo i hm is in oduced. ST i- Gen is a new inc emen al lea ning me hod ha inds g oups o simila beha iou pa e ns in 3D s eam da a con inuously. In his pape , ST iGen algo i hm is applied o HCS images o ge in o ma- ion om images in eal- ime. The a icle is s uc u ed as ollows: ST iGen and HCS me hod- ologies a e p esen ed in Sec ion 2; he expe imen al se up and he yielded esul s in Sec ion 3; and inally he conclusions a e in Sec- ion 4. 2 METHODOLOGY 2.1 ST iGen me hodology ST iGen is a new inc emen al lea ning me hod ha c ea es iclus- e s (based on T iGen algo i hm [ 8 ]) and keeps hem upda ed aking in o accoun he knowledge om p e ious s eams and upg ading i s lea ning me hod. ST iGen mee s all 4 Da a S eaming equi e- men s, i.e., da a has o be p ocessed in he o de o i s a i al and one by one; he lea ning model has o be upda ed inc emen ally; he model has o deal wi h small amoun o memo y e e ing o he huge quan i y o da a and i has o be as . ST iGen algo i hm is inspi ed in he o line/online app oach o s eam algo i hms. Consequen ly, ST iGen s a s wi h an execu ion o he T iGen modi ied o ea s eam da a as s a ic da a wi h W da a, whe e W is he maximum numbe o da a s eams ha can be used in an i e a ion o he ST iGen algo i hm. A e wa ds, he algo i hm p ocesses each new da a s eam and he model upda es inc emen ally and quickly basing i sel on he new s eams in o de o p o ide he upg aded iclus e s. Du ing his second phase o ST iGen, i ies o ex end he ac ual iclus e s h oughou ime emo ing he "oldes " ime poin o keep always a maximum o Wda a poin s, a egula p ocedu e in Da a S eaming algo i hms [ 6 ]. In addi ion o he ex ension o he ac ual iclus e s o e ime, he lea ning model adjus s i sel inc emen ally depending on he GRQ (GRaphical Quali y) measu e, pa o one o he T iGen i ness unc ions [7]. Mo e speci ically, he ST iGen lea ning model makes mu a ions in o iclus e s, i.e., adding, dele ing and/o changing bo h ins ances and/o a ibu es, o be upda ed. These ope a ions a e quicke han e- aining T iGen each ime a new s eam a i es o keep he lea ning model upda ed. Mu a ions allow o ind cu en and global eal solu ions as ST iGen depends on he esul s om he i s execu ion o T iGen ha can change e e y ime i is execu ed. In his way, iclus e s a e mu a ed un il hei GRQ alues a e highe han minGRQ o un il he numbe o i e a ions made is highe han numI . In his way, ST iGen ies o include o emo e some cu en iclus e ’s componen s in o de o keep mos accu a e componen s. These 2 pa ame e s and also he "window" Wpa ame e and a minimum GRQ h eshold ( ha can dele e he oldes ime included in he iclus e when i s GRQ is smalle han his) ake di e en alues depending on he da ase , o be able o adjus o small o /and ab up changes in s eams. When he da ase is syn he ic o when he esul ing iclus e s a e known in ad ance, a alida ion p ocess o compa e ounded and eal iclus e s is done wi h accu acy and F1 Sco e measu es. Da ase s ha a e nei he syn he ic no wi h known iclus e s in ad ance, a e e alua ed depending on he GRQ alue. 2.2 High-Con en Sc eening me hodology High-Con en Sc eening o Analysis (HCS o HCA) is gaining im- po ance. HCS combines au oma ed imaging acquisi ion and image analysis using, mos equen ly, au oma ed luo escence mic oscopy [ 4 ]. Du ing he HCS p ocess in ac cells a e incuba ed wi h sub- s ances ha al e hei pheno ype in a desi ed way [ 3 ]. These cells a e sc eened and mul iple luo escence eadou s a e measu ed in pa allel. This p ocess p o ides big olume o da a wi h high bio- logical in o ma ion con en . Subcellula loca ions and luo escence colou in ensi y du ing di e en complex cellula e en s, in e ms o space and ime, a e measu ed wi h he au oma ed image analysis phase o HCS [ 5 ]. HCS images ha e been use ul o de ec and s udy DNA, cy okinesis, cell di ision, cell mig a ion, apop osis, mi osis, and mo e cellula e en s o a ge componen s. HCS p ocesses in ol e di e en asks as cell p epa a ion and labelling, image acquisi ion, image analysis and da a managemen [ 9 ]. In e ms o da a challenges, HCS has 2 p incipal issues: da a s o age and da a p ocessing. One o he main in e es ing poin s o HCS is he simul aneous analysis o images ha equi es a high compu ing powe and quickly compu e ne wo k connec ions [1]. 2.3 ST iGen applica ion o High-Con en Sc eening images Applying ST iGen o HCS images allows o disco e he bes ea- u es o g oup o ge in o ma ion om cells. Ac ual nume ical ea- u es ex ac ed om HCS images a e: 1) luo escen ma ke colou ; 2) cell componen ’s shape; 3) spa ial si ua ion; 4) dis ibu ion o Figu e 1: Example o p ep ocessing phase o HCS images pixel-colou in a cell egion o in e es o s udy in e ac ions o co-occu ences [ 11 ]. Mo eo e , e olu ion h oughou ime is added o hese 4 ea u es when applying ST iGen o HCS. The ype o images used in his expe imen a e indi idual in ac cells luo escen mic oscopy-based HCS images. Images a e p o- cessed in o de o c ea e a da ase ha i s ST iGen equi emen s. Fi s ly, each RGB image is ans o med in o a 3D ma ix ep esen - ing he coo dina es o each image pixel in decimal numbe s. In o he wo ds, alues ha ep esen images colou s a e he pixel-colou o luo escen -ma ke colou ea u es men ioned abo e. A il e is passed h ough e e y image o no include any image ha p esen noise due o op ic abe a ions, mic oscopy issues o e en bad ac- quisi ion o he image. Secondly, da a is p epa ed o i ST iGen da ase speci ica ions, i.e., aking in o accoun : de ec ing he colou s speci ied o analyse and de ec ing a eas limi s ( o a eas wi h mo e han an use ixed alue). In addi ion, andom alues be ween 0 and 1000 a e added in o de o make ST iGen able o igno e he backg ound o images. A g aphical example o his me hodology is in Fig. 1. 3 RESULTS AND DISCUSSION In his sec ion, he esul s ob ained by he applica ion o he ST iGen algo i hm o a HCS images da ase a e p esen ed. ST iGen has been applied o a se o HCS images om [ 2 ] o HeLa cells (ce ical cance cells aken om a woman in he 50s ha can di ide hemsel es an unlimi ed numbe o imes in well p e- se ed labo a o y condi ions [ 10 ]). Fo his expe imen , he da ase selec ed is he one ha p esen s he eac ion o HeLa cells o T ans- e in ecep o s (usually applied as cance cell a ge because hey enhances si e-speci ic he apies). The quali y o he esul ing i- clus e s is e alua ed wi h he GRQ measu e. In addi ion, o his expe imen , a da ase wi h he desi ed ST iGen ounded iclus- e s has been c ea ed in o de o compu e F1 Sco e and Accu acy measu es o check he pe o mance o he algo i hm. 460 HCS images simila o he abo e image in Fig. 1 ha e been used o his expe imen wi h black o he backg ound image, blue o he cell bo de , ed o he nuclea DNA and g een o he endosome compa men . Cell bo de s (blue colou ) has been igno e because hey do no p o ide ex a in o ma ion abou HCS ea u es. Table 1: ST iGen con igu a ion pa ame e s Execu ion minGRQ h esholdGRQ numI 1 0.95 0.80 10 2 0.88 0.70 15 3 0.90 0.75 20 Figu e 2: F1 Sco e esul s Figu e 3: Accu acy esul s Figu e 4: ST iGen execu ion ime ST iGen has been execu ed 3 imes due o he ac ha he i s iclus e s esul s depend on he i s iclus e s ounded by T iGen. In ha way, con igu a ion pa ame e s ake di e en alues in each execu ion o see he in luence o hem in he esul s (see Table 1) excep ing W ha has as maximum alue 3. ST iGen pe o mance esul s a e in he ollowing igu es: Fig. 3 shows accu acy alues o all iclus e s and Fig. 2 shows F1 sco e alues. Figu es show ha he algo i hm pe o ms in an accu a e way and can ind componen s co ec ly. F1 sco e o he g een iclus e s in all 3 execu ions a ies a lo , i is due o he ac ha g een a eas sp ead h ough cells and a e in con inuous mo emen , howe e he mean accu acy alue in all 3 execu ions is 0.908. Red a eas a e mo e s able in bo h measu es because hey a e in a simila posi ion in all s eams. Apa om hese measu es, ano he impo an pa ame e ha ep esen s he good pe o mance o he algo i hm is ime execu ion. This expe imen has been done in a compu e wi h an i7-5820K 3.3GHz p ocesso and 48GB RAM memo y. The i s phase o ST i- Gen ( ha is jus one execu ion o T iGen wi h he i s 10 s eams) akes a mean o 8.9 minu es o execu e comple ely. A e wa ds, he Da a S eaming phase s a s and each new image s eam is p ocessed and p o ides ounded iclus e s in a mean o 16 seconds. In gene al, he quali y measu es a e mos ly equal in he 3 ex- ecu ions. Howe e , i can be seen ha he execu ion ime o he 3 d execu ion o ST iGen is much smalle , due mos ly o he small alue o numI . 4 CONCLUSIONS The ST iGen algo i hm pe o ms wi h good esul s when dealing wi h s eam da a as we ha e seen in Sec ion 3. I is as e han execu ing he T iGen algo i hm when a new s eam a i es and so he e olu ion o da a h oughou ime can be analysed in eal- ime. I p o es ha he lea ning model upda es inc emen ally making mu a ions and aking in o accoun he Wmos ecen s eams. The applica ion o HCS images in o he Da a S eaming en i on- men wi h ST iGen leads he possibili y o ob aining in o ma ion abou he e olu ion o HCS ea u es, i.e. componen s colou , shape, loca ion and dis ibu ion, h oughou ime in eal- ime. A possi- ble applica ion o his expe imen would be he ac ha ST iGen could de ec when ex e nal agen s like subs ances, d ugs, an ibod- ies, e c a e added o he exposed cell and how he cell eac s o hem, e.g., changing iclus e s componen s. I allows o do a con inuous analysis o he cell in eal- ime. ACKNOWLEDGMENTS Au ho s hank he Spanish Minis y o Economy and Compe i i e- ness o he suppo unde he p ojec TIN2017-88209-C2-1-R and TIN2017-88209-C2-2-R. REFERENCES [1] M. Bickle. 2008. High-con en sc eening : A new p ima y sc eening ool ? 11, 11 (2008). [2] CellO gnize P ojec [n. d.]. CellO ganize p ojec om Ca negie Mellon Uni e - si y. h p://www.cello ganize .o g/2d-hela/ [3] D. C onk. 2013. Chap e 8 - High- h oughpu sc eening. (2013), 95 – 117. h ps: //doi.o g/10.1016/B978-0-7020-4299-7.00008-1 [4] R. Flaumenha . 2007. 3.07 - Chemical Biology. (2007), 129 – 149. h ps://doi. o g/10.1016/B0-08-045044-X/00080-8 [5] G. Galea and J. C. Simpson. 2013. Chap e 17 - High-Con en Sc eening and Analysis o he Golgi Complex. 118 (2013), 281 – 295. h ps://doi.o g/10.1016/ B978-0-12-417164-0.00017-3 [6] M. Ghesmoune, M. Lebbah, and H. Azzag. 2016. S a e-o - he-a on clus e ing da a s eams. Big Da a Analy ics 1, 1 (2016), 1–27. h ps://doi.o g/10.1186/ s41044-016-0011-3 [7] D. Gu ié ez-A ilés, R. Gi áldez, F.J. Gil-Cumb e as, and C. Rubio-Escude o. 2018. TRIQ: A new me hod o e alua e iclus e s. BioDa a Mining 11, 1 (2018), 1–29. h ps://doi.o g/10.1186/s13040-018-0177-5 [8] D. Gu ié ez-A ilés, C. Rubio-Escude o, F. Ma ínez-Ál a ez, and J.C. Riquelme. 2014. T iGen: A gene ic algo i hm o mine iclus e s in empo al gene exp ession da a. Neu ocompu ing 132 (2014), 42–53. h ps://doi.o g/10.1016/j.neucom.2013. 03.061 [9] S. Lee and B. J. Howell. 2006. [25] - High-Con en Sc eening: Eme ging Ha dwa e and So wa e Technologies. 414 (2006), 468 – 483. h ps://doi.o g/10.1016/ S0076-6879(06)14025-2 [10] B.P. Lucey, W.A. Nelson-Rees, and G.M. Hu chins. 2009. Hen ie a Lacks, HeLa cells, and cell cul u e con amina ion. A chi es o Pa hology and Labo a o y Medicine 133, 9 (2009), 1463–1467. [11] F. Heigwe M. Bou os and C. Lau e . 2015. Mic oscopy-based High-con en Sc eening. Cell 163, 6 (2015), 1314–1325. h ps://doi.o g/10.1016/j.cell.2015.11.007 [12] L. Melga -Ga cía, D. Gu ié ez-A ilés, and C. Rubio-Escude o. 2019. Disco - e ing Beha io Pa e ns in Big Da a S eaming En i onmen s : The ST iGen Me hodology. (2019). Manusc ip submi ed o publica ion.